Network Working Group                                             X. Zhu, Zhu
Internet-Draft                                                    R. Pan
Internet Draft
Intended status: Experimental                                 M. A. Ramalho, Ramalho
Expires: April 11, 2016                                          S. Mena
Intended Status: Informational                  C. Ganzhorn,
                                                                P. E. Jones
Expires: October 29, 2015
                                                                   J. Fu
                                                           Cisco Systems
                                                             S. De Aronco
                                Ecole Polytechnique Federale de Lausanne
                                                          April 28, D'Aronco
                                                                    EPFL
                                                             C. Ganzhorn
                                                         October 9, 2015

     NADA: A Unified Congestion Control Scheme for Real-Time Media
                        draft-ietf-rmcat-nada-00
                        draft-ietf-rmcat-nada-01

Abstract

   Network-Assisted Dynamic Adaptation (NADA) is

   This document describes NADA (network-assisted dynamic adaptation), a
   novel congestion control scheme for interactive real-time media
   applications, such as video conferencing.  In NADA, the proposed scheme,
   the sender regulates its sending rate based on either implicit or
   explicit congestion signaling signaling, in a
   consistent manner. As one example of explicit signaling, NADA unified approach.  The scheme can
   benefit from explicit congestion notification (ECN) markings from
   network nodes.  It also maintains consistent sender behavior in the
   absence of explicit signaling such markings, by reacting to queuing delay delays and packet
   loss.

   This document describes the overall system architecture for NADA, as
   well as recommended behavior at the sender and the receiver.
   losses instead.

Status of this This Memo

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   Copyright (c) 2012 2015 IETF Trust and the persons identified as the
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  System Model  . . Overview . . . . . . . . . . . . . . . . . . . . . . .   3
   4. NADA Receiver Behavior  . . . . . .  Core Congestion Control Algorithm . . . . . . . . . . . . . .   4
     4.1 Estimation of one-way delay and queuing delay
     4.1.  Mathematical Notations  . . . . . . .  4
     4.2 Estimation of packet loss/marking ratio . . . . . . . . . .   5
     4.3 Non-linear warping of delay  .
     4.2.  Receiver-Side Algorithm . . . . . . . . . . . . . . .  6
     4.4 Aggregating congestion signals . .   7
     4.3.  Sender-Side Algorithm . . . . . . . . . . . . .  7
     4.5 Estimating receiving rate . . . . .   9
   5.  Practical Implementation of NADA  . . . . . . . . . . . .  7
     4.6 Sending periodic feedback . .  10
     5.1.  Receiver-Side Operation . . . . . . . . . . . . . . .  7
     4.7 Discussions on delay metrics . .  10
       5.1.1.  Estimation of one-way delay and queuing delay . . . .  11
       5.1.2.  Estimation of packet loss/marking ratio . . . . . . .  11
       5.1.3.  Estimation of receiving rate  . . .  8
   5. NADA Sender Behavior . . . . . . . . .  11
     5.2.  Sender-Side Operation . . . . . . . . . . . .  9
     5.1 Reference rate calculation . . . . . .  12
       5.2.1.  Rate shaping buffer . . . . . . . . . . . 10
       5.1.1 Accelerated ramp up . . . . . .  12
       5.2.2.  Adjusting video target rate and sending rate  . . . .  13
   6.  Discussions and Further Investigations  . . . . . . . . 10
       5.1.2. Gradual rate update . . .  13
     6.1.  Choice of delay metrics . . . . . . . . . . . . . . . 11
     5.2 Video encoder rate control . .  13
     6.2.  Method for delay, loss, and marking ratio estimation  . .  14
     6.3.  Impact of parameter values  . . . . . . . . . . . . . 12
     5.3 Rate shaping buffer . .  14
     6.4.  Sender-based vs. receiver-based calculation . . . . . . .  15
     6.5.  Incremental deployment  . . . . . . . . . . . 12
     5.4 Adjusting video target rate and sending rate . . . . . . . . 12
   6. Incremental Deployment  16
   7.  Implementation Status . . . . . . . . . . . . . . . . . . . . 13
   7. Implementation Status  16
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
   8. IANA Considerations  16
   9.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . 14
   9.  17
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . . . 14
     9.1  17
     10.1.  Normative References . . . . . . . . . . . . . . . . . . . 14
     9.2  17
     10.2.  Informative References . . . . . . . . . . . . . . . . . . 14  17
   Appendix A.  Network Node Operations  . . . . . . . . . . . . . . . 15
     A.1  19
     A.1.  Default behavior of drop tail queues  . . . . . . . . . . . . . . . 16
     A.2  19
     A.2.  RED-based ECN marking . . . . . . . . . . . . . . . . . . . . . . . . 16
     A.3 PCN marking  . . . . . . . . . . . . . . . .  19
     A.3.  Random Early Marking with Virtual Queues  . . . . . . . . 16  20

   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . . . 17  21

1.  Introduction

   Interactive real-time media applications introduce a unique set of
   challenges for congestion control.  Unlike TCP, the mechanism used
   for real-time media needs to adapt quickly to instantaneous bandwidth
   changes, accommodate fluctuations in the output of video encoder rate
   control, and cause low queuing delay over the network.  An ideal
   scheme should also make effective use of all types of congestion
   signals, including packet loss, queuing delay, and explicit
   congestion notification (ECN) [RFC3168] markings.

   Based on  The requirements
   for the above considerations, this congestion control algorithm are outlined in
   [I-D.ietf-rmcat-cc-requirements].

   This document describes a an experimental congestion control scheme
   called network-assisted dynamic adaptation (NADA).  The NADA design
   benefits from explicit congestion control signals (e.g., ECN
   markings) from the network, yet also operates when only implicit
   congestion indicators (delay and/or loss) are available.  In
   addition, it supports weighted bandwidth sharing among competing
   video flows.

   This documentation describes the overall system architecture,
   recommended designs at the sender and receiver, as well as expected
   network node operations.  The signaling mechanism consists of standard RTP
   timestamp [RFC3550] and standard RTCP feedback reports.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

3.  System Model

   The overall system consists of Overview

   Figure 1 shows the following elements:

        * Source end-to-end system for real-time media stream, in the form of consecutive raw video
        frames and audio samples;

        * transport
   that NADA operates in.

     +---------+  r_vin  +--------+        +--------+     +----------+
     |  Media  |<--------|  RTP   |        |Network |     |   RTP    |
     | Encoder |========>| Sender |=======>|  Node  |====>| Receiver |
     +---------+  r_vout +--------+ r_send +--------+     +----------+
                             /|\                                |
                              |                                 |
                              +---------------------------------+
                                    RTCP Feedback Report

                         Figure 1: System Overview

   o  Media encoder with rate control capabilities.  It takes encodes the
      source media stream and encodes it to into an RTP stream at a with target bit rate R_v. Note that the r_vin.
      The actual output rate from the encoder
        R_o r_vout may fluctuate
      around the target R_v. Also, r_vin.  In addition, the encoder can only change
      its bit rate at rather coarse time intervals, e.g., once every 0.5
      seconds.

        *

   o  RTP sender, sender: responsible for calculating the target bit NADA reference rate
        R_n
      based on network congestion indicators (delay, loss, or ECN
      marking reports from the receiver), for updating the video encoder
      with a new target rate R_v, r_vin, and for regulating the actual
      sending rate R_s r_send accordingly. A rate shaping buffer is
        employed to absorb the instantaneous difference between video
        encoder output rate R_v and sending rate R_s. The buffer size
        L_s, together with R_n, influences the calculation of actual
        sending rate R_s and video encoder target rate R_v.  The RTP sender also generates provides an
      RTP timestamp in for each outgoing packets.

        * packet.

   o  RTP receiver, receiver: responsible for measuring and estimating end-to-
        end end-to-end
      delay based on sender RTP timestamp. In the presence of
        packet loss and ECN markings, it keeps track of timestamp, packet loss and ECN marking ratios.
      ratios, as well as receiving rate (r_recv) of the flow.  It
      calculates the equivalent delay x_n aggregated congestion signal (x_n) that accounts
      for queuing delay, ECN marking, and packet loss, as
        well as losses, and determines
      the derivative (i.e., mode for sender rate of change) of this congestion
        signal as x'_n. adaptation (rmode) based on whether the
      flow has encountered any standing non-zero congestion.  The
      receiver feeds both pieces of information
        (x_n and x'_n) sends periodic RTCP reports back to the sender via periodic RTCP reports.

        * sender,
      containing values of x_n, rmode, and r_recv.

   o  Network node, node with several modes of operation.  The system can work
      with the default behavior of a simple drop tail queue.  It can
      also benefit from advanced AQM features such as PIE, FQ-CoDel,
      RED-based ECN marking, and PCN marking using a token bucket
      algorithm.  Note that network node operation is out of scope for
      the design of NADA.

4.  Core Congestion Control Algorithm

   Like TCP-Friendly Rate Control (TFRC) [Floyd-CCR00] [RFC5348], NADA
   is a rate-based congestion control algorithm.  In its simplest form,
   the following, we will elaborate on the respective operations at sender reacts to the
NADA receiver and sender.

4. NADA Receiver Behavior

The receiver continuously monitors end-to-end per-packet statistics in
terms of delay, loss, and/or ECN marking ratios. It then aggregates all
forms collection of network congestion indicators into
   in the form of an equivalent delay aggregated congestion signal, and
periodically reports this back to operates in one
   of two modes:

   o  Accelerated ramp-up: when the sender. In addition, bottleneck is deemed to be
      underutilized, the receiver
tracks rate increases multiplicatively with respect to
      the receiving rate of the flow previously successful transmissions.  The rate
      increase mutliplier (gamma) is calculated based on observed round-
      trip-time and includes that in the target feedback
message.

4.1 Estimation of one-way delay and queuing delay

The delay estimation process in NADA follows a similar approach as in
earlier delay-based congestion control schemes, such as LEDBAT
[RFC6817]. NADA estimates the forward delay interval, so as having a constant base
delay component plus a time varying to limit self-
      inflicted queuing delay component. The base
delay is estimated as delay.

   o  Gradual rate update: in the minimum value of one-way delay observed over a
relatively long period (e.g., tens presence of minutes), whereas non-zero aggregate
      congestion signal, the individual
queuing delay value sending rate is taken adjusted in reaction to be the difference between one-way delay
      both its value (x_n) and base delay.

In its change in value (x_diff).

   This section introduces the list of mathematical terms, for packet n arriving notations and
   describes the core congestion control algorithm at the sender and
   receiver, one-way
delay is calculated as:

                         z_n = t_r,n - t_s,n,

where t_s,n and t_r,n are sender and receiver timestamps, respectively.
A real-world implementation should also properly handle  Additional details on recommended practical issues
such as wrap-around
   implementations are described in Section 5.1 and Section 5.2.

4.1.  Mathematical Notations

   This section summarizes the value list of z_n, which are omitted from the
above simple expression for brevity.

The base delay, d_f, is estimated as variables and parameters used in
   the minimum value of previously
observed z_n's over NADA algorithm.

     +--------------+-------------------------------------------------+
     | Notation     | Variable Name                                   |
     +--------------+-------------------------------------------------+
     | t_curr       | Current timestamp                               |
     | t_last       | Last time sending/receiving a relatively long period. This assumes that the
drift feedback          |
     | delta        | Observed interval between sending current and receiving clocks remains bounded by a small
value.

Correspondingly, the queuing delay experienced by the packet n is
estimated as:

                           d_n previous  |
     |              | feedback reports: delta = z_n - d_f.

The individual sample values of queuing t_curr-t_last         |
     | r_n          | Reference rate based on network congestion      |
     | r_send       | Sending rate                                    |
     | r_recv       | Receiving rate                                  |
     | r_vin        | Target rate for video encoder                   |
     | r_vout       | Output rate from video encoder                  |
     | d_base       | Estimated baseline delay should be further                        |
     | d_fwd        | Measured and filtered
against various non-congestion-induced noise, such as spikes due to
processing "hiccup" at the network nodes. We denote the resulting
queuing one-way delay value as d_hat_n.

Our current implementation employs a simple 5-point median filter over
per-packet queuing             |
     | d_n          | Estimated queueing delay estimates, followed by an exponential smoothing
filter. We have found such relatively simple treatment to suffice in
guarding against processing                        |
     | d_tilde      | Equivalent delay outliers observed in wired
connections. For wireless connections with a higher after non-linear warping       |
     | p_mark       | Estimated packet delay
variation (PDV), more sophisticated techniques on de-noising, outlier
rejection, and trend analysis may be needed.

Like other delay-based ECN marking ratio              |
     | p_loss       | Estimated packet loss ratio                     |
     | x_n          | Aggregate congestion control schemes, performance signal                     |
     | x_prev       | Previous value of aggregate congestion signal   |
     | x_diff       | Change in aggregate congestion signal w.r.t.    |
     |              | its previous value: x_diff = x_n - x_prev       |
     | rmode        | Rate update mode: (0 = accelerated ramp-up;     |
     |              | 1 = gradual update)                             |
     | gamma        | Rate increase multiplier in accelerated ramp-up |
     |              | mode                                            |
     | rtt          | Estimated round-trip-time at sender             |
     | buffer_len   | Rate shaping buffer occupancy measured in bytes |
     +--------------+-------------------------------------------------+

                       Figure 2: List of variables.

    +---------------+---------------------------------+----------------+
    | Notation     | Parameter Name                   | Default Value  |
    +--------------+----------------------------------+----------------+
    | PRIO         | Weight of priority of NADA
depends on the accuracy flow   |    1.0
    | RMIN         | Minimum rate of its delay measurement and estimation module.
Appendix A application      |    150 Kbps    |
    |              | supported by media encoder       |                |
    | RMAX         | Maximum rate of application      |    1.5 Mbps    |
    |              | supported by media encoder       |                |
    | X_REF        | Reference congestion level       |    20ms        |
    | KAPPA        | Scaling parameter for gradual    |    0.5         |
    |              | rate update calculation          |                |
    | ETA          | Scaling parameter for gradual    |    2.0         |
    |              | rate update calculation          |                |
    | TAU          | Upper bound of RTT in [RFC6817] provides an extensive discussion gradual    |    500ms       |
    |              | rate update calculation          |                |
    | DELTA        | Target feedback interval         |    100ms       |
    | LOGWIN       | Observation window in time for   |    500ms       |
    |              | calculating packet summary       |                |
    |              | statistics at receiver           |                |
    | QEPS         | Threshold for determining queuing|     10ms       |
    |              | delay build up at receiver       |                |
    +..............+..................................+................+
    | QTH          | Delay threshold for non-linear   |    100ms       |
    |              | warping                          |                |
    | QMAX         | Delay upper bound for non-linear |    400ms       |
    |              | warping                          |                |
    | DLOSS        | Delay penalty for loss           |    1.0s        |
    | DMARK        | Delay penalty for ECN marking    |    200ms       |
    +..............+..................................+................+
    | GAMMA_MAX    | Upper bound on this aspect.

4.2 Estimation rate increase     |     20%        |
    |              | ratio for accelerated ramp-up    |                |
    | QBOUND       | Upper bound on self-inflicted    |    50ms        |
    |              | queuing delay during ramp up     |                |
    +..............+..................................+................+
    | FPS          | Frame rate of packet loss/marking ratio

The receiver detects packet losses via gaps incoming video     |     30         |
    | BETA_S       | Scaling parameter for modulating |    0.1         |
    |              | outgoing sending rate            |                |
    | BETA_V       | Scaling parameter for modulating |    0.1         |
    |              | video encoder target rate        |                |
    | ALPHA        | Smoothing factor in the RTP sequence numbers exponential  |    0.1         |
    |              | smoothing of received packets. It then calculates instantaneous packet loss ratio
as the ratio between the number of missing packets over the number and     |                |
    |              | marking ratios                   |
    +--------------+----------------------------------+----------------+

                  Figure 3: List of
total transmitted packets in the given time window (e.g., during the
most recent 500ms). This instantaneous value is passed over an
exponential smoothing filter, algorithm parameters.

4.2.  Receiver-Side Algorithm

   The receiver-side algorithm can be outlined as below:

  On initialization:
    set d_base = +INFINITY
    set p_loss = 0
    set p_mark = 0
    set r_recv = 0
    set both t_last and the filtered result is reported back
to the sender t_curr as the observed current time

  On receiving a media packet:
    obtain current timestamp t_curr
    obtain from packet loss ratio p_L.

We note that more sophisticated methods in header sending time stamp t_sent
    obtain one-way delay measurement: d_fwd = t_curr - t_sent
    update baseline delay: d_base = min(d_base, d_fwd)
    update queuing delay:  d_n = d_fwd - d_base
    update packet loss ratio
calculation, such as that adopted by TFRC [Floyd-CCR00], will likely be
beneficial. These alternatives are currently under investigation.

Estimation of estimate p_loss
    update packet marking ratio p_M, when ECN is enabled at
bottleneck network nodes along the path, will follow the same procedure
as above. Here it is assumed that ECN marking information at the IP
header are somehow passed along to the transport layer by the estimate p_mark
    update measurement of receiving
endpoint.

4.3 Non-linear rate r_recv

  On time to send a new feedback report (t_curr - t_last > DELTA):
    calculate non-linear warping of delay delay d_tilde if packet loss exists
    calculate aggregate congestion signal x_n
    determine mode of rate adaptation for sender: rmode
    send RTCP feedback report containing values of: rmode, x_n, and r_recv
    update t_last = t_curr

   In order for a delay-based flow to hold its ground and sustain a
reasonable share of bandwidth in the presence of a when competing
   against loss-based flow flows (e.g., loss-based TCP), it is important to
   distinguish between different levels of observed queuing delay.  For
   instance, a moderate queuing delay value below 100ms is likely self-inflicted self-
   inflicted or induced by other delay-
based delay-based flows, whereas a high
   queuing delay value of several hundreds of milliseconds may indicate
   the presence of a loss-based flow that does not refrain from
   increased delay.

Inspired

   When packet losses are observed, the estimated queuing delay follows
   a non-linear warping inspired by the delay-adaptive congestion window
   backoff policy in
[Budzisz-TON11] -- the work by itself is a window-based congestion
control scheme with fair coexistence with TCP -- we devise the following
non-linear warping of estimated queuing delay value:

        d_tilde_n = (d_hat_n), [Budzisz-TON11]:

                / d_n,                  if d_hat_n < d_th;

                         (d_max     d_n<QTH;
                |
                |     (QMAX - d_hat_n)^4
        d_tilde_n d_n)^4
     d_tilde = d_th --------------------, <  QTH ----------------, if  d_th<d_hat_n<d_max;
                          (d_max QTH<d_n<QMAX  (1)
                |     (QMAX - d_th)^4

        d_tilde_n = QTH)^4
                |
                \  0,   if d_hat_n > d_max.                   otherwise.

   Here, the queuing delay value is unchanged when it is below the first
   threshold d_th; QTH; it is discounted scaled down following a non-linear curve when
   its value falls between d_th QTH and d_max; QMAX; above d_max, QMAX, the high queuing
   delay value no longer counts toward congestion control.

When queuing delay is in the range (0, d_th), NADA operates in pure
delay-based mode if no losses/markings are present. When queuing delay
exceeds d_max, NADA reacts to loss/marking only. In between d_th and
d_max, the sending rate will converge and stabilize at an operating
point with a fairly high queuing delay and non-zero packet loss ratio.

In our current implementation d_th is chosen as 50ms and d_max is chosen
as 400ms. The impact of the choice of d_th and d_max will be
investigated in future work.

4.4 Aggregating congestion signals

   The receiver aggregates all three forms of aggregate congestion signal in terms of
an equivalent delay: is:

       x_n = d_tilde_n d_tilde + p_M*d_M p_mark*DMARK + p_L*d_L,      (1)

where d_M p_loss*DLOSS.      (2)

   Here, DMARK is a prescribed fictitious delay value penalty associated with ECN markings (e.g., d_M = 200 ms),
   and d_L DLOSS is a prescribed fictitious delay
value penalty associated with packet losses (e.g., d_L = 1 second). By
introducing a large fictitious delay penalty for ECN marking and packet
loss, the proposed scheme leads to low end-to-end actual delay in the
presence of such events.

While the losses.
   The value of d_M and d_L are fixed DLOSS and predetermined in the
current design, a scheme for automatically tuning these values based DMARK does not depend on
desired bandwidth sharing behavior in configurations at the presence
   network node, but does assume that ECN markings, when available,
   occur before losses.  Furthermore, the values of other competing
loss-based DLOSS and DMARK need
   to be set consistently across all NADA flows (e.g., loss-based TCP) is being studied. for them to compete
   fairly.

   In the absence of ECN marking from the network, packet marking and losses, the value of x_n falls
back reduces
   to the observed queuing delay d_n for packet n when queuing delay
is low and no packets are lost over a lightly congested path. d_n.  In that case the NADA algorithm
   operates in purely delay-based mode.

4.5 Estimating receiving rate

Estimation of receiving rate the regime of delay-based adaptation.

   Given observed per-packet delay and loss information, the flow receiver is fairly straightforward. NADA
maintains
   also in a good position to determine whether the network is
   underutilized and recommend the corresponding rate adaptation mode
   for the sender.  The criteria for operating in accelerated ramp-up
   mode are:

   o  No recent packet losses within the observation window of 500ms, LOGWIN; and simply divides the
total size

   o  No build-up of packets arriving during that queuing delay: d_fwd-d_base < QEPS for all previous
      delay samples within the observation window over LOGWIN.

   Otherwise the time span. algorithm operates in graduate update mode.

4.3.  Sender-Side Algorithm

   The sender-side algorithm is outlined as follows:

     on initialization:
       set r_n = RMIN
       set rtt = 0
       set x_prev = 0
       set t_last and t_curr as current time

     on receiving feedback report:
       obtain current timestamp: t_curr
       obtain values of rmode, x_n, and r_recv from feedback report
       update estimation of rtt
       measure feedback interval: delta = t_curr - t_last
       if rmode == 0:
         update r_n following accelerated ramp-up rules
       else:
         update r_n following gradual update rules
       clip rate r_n within the range of [RMIN, RMAX]
       x_prev = x_n
       t_last = t_curr

   In accelerated ramp-up mode, the rate r_n is denoted updated as R_r.

4.6 Sending periodic follows:

                                QBOUND
       gamma = min(GAMMA_MAX, -----------)     (3)
                               rtt+DELTA

       r_n  =  (1+gamma) r_recv             (4)

   The rate increase multiplier gamma is calculated as a function of
   upper bound of self-inflicted queuing delay (QBOUND), round-trip-time
   (rtt), and target feedback

Periodically, the receiver feeds back interval DELTA.  It has a tuple maximum value of
   GAMMA_MAX.  The rationale behind (3)-(4) is that the longer it takes
   for the sender to observe self-inflicted queuing delay build-up, the
   more conservative the sender should be in increasing its rate, hence
   the smaller the rate increase multiplier.

   In gradual update mode, the rate r_n is updated as:

       x_offset = x_n - PRIO*X_REF*RMAX/r_n           (5)

       x_diff   = x_n - x_prev                        (6)

                          delta    x_offset
       r_n = r_n - KAPPA*-------*------------*r_n
                           TAU       TAU

                              x_diff
                 - KAPPA*ETA*---------*r_n            (7)
                               TAU

   The rate changes in proportion to the previous rate decision.  It is
   affected by two terms: offset of the most recent values aggregate congestion signal from
   its value at equilibrium (x_offset) and its change (x_diff).
   Calculation of x_offset depends on maximum rate of <d_hat_n, x_n, x'_n, R_r> in RTCP feedback messages to aid the sender
in flow (RMAX),
   its calculation weight of target rate. priority (PRIO), as well as a reference congestion
   signal (X_REF).  The queuing delay value d_hat_n of X_REF is
included along with chosen that the maximum rate
   of RMAX can be achieved when the observed congestion signal level is
   below PRIO*X_REF.

   At equilibrium, the composite aggregated congestion signal stablizes at x_n so =
   PRIO*X_REF*RMAX/r_n.  This ensures that when multiple flows share the
sender can decide whether the network is truly underutilized (see Sec.
6.1.1 Accelerated ramp-up).

The
   same bottleneck and observe a common value of x'_n corresponds x_n, their rates at
   equilibrium will be proportional to the derivative (i.e., their respective priority levels
   (PRIO) and maximum rate of change)
of (RMAX).

   As mentioned in the composite congestion signal:

                x_n - x_(n-k)
        x'_n = ---------------,           (2)
                  delta

where sender-side algorithm, the interval between consecutive RTCP feedback messages final rate is denoted
as delta. The packet indices corresponding to clipped
   within the current and previous
feedback are n and (n-k), respectively.

The choice of target feedback interval needs to strike dynamic range specified by the right balance application:

           r_n = min(r_n, RMAX)          (8)

           r_n = max(r_n, RMIN)          (9)

   The above operations ignore many practical issues such as clock
   synchronization between timely feedback sender and low RTCP feedback message counts. Through
simulation studies receiver, filtering of noise in
   delay measurements, and frequency-domain analysis, it was determined that
a feedback interval below 250ms base delay expiration.  These will not break up the feedback control
loop be
   addressed in later sections describing practical implementation of
   the NADA congestion control algorithm. Thus, it is recommended
to use a target feed interval

5.  Practical Implementation of 100ms. This will result NADA

5.1.  Receiver-Side Operation

   The receiver continuously monitors end-to-end per-packet statistics
   in a feedback
bandwidth terms of 16Kbps with 200 bytes per feedback message, less than 0.1%
overhead for a 1Mbps flow.

4.7 Discussions on delay metrics

The current design works with relative one-way-delay (OWD) as the main
indication delay, loss, and/or ECN marking ratios.  It then
   aggregates all forms of congestion. The value congestion indicators into the form of an
   equivalent delay and periodically reports this back to the relative OWD is obtained by
maintaining sender.

   In addition, the minimum value receiver tracks the receiving rate of observed OWD over a relatively long
time horizon the flow and subtract
   includes that out from the observed absolute OWD value.
Such an approach cancels out the fixed difference between in the sender feedback message.

5.1.1.  Estimation of one-way delay and
receiver clocks. It has been widely adopted by other queuing delay

   The delay estimation process in NADA follows a similar approach as in
   earlier delay-based congestion control approaches schemes, such as LEDBAT
   [RFC6817]. As discussed in
[RFC6817],  NADA estimates the forward delay as having a constant
   base delay component plus a time horizon for tracking varying queuing delay component.
   The base delay is estimated as the minimum OWD needs to be
chosen with care: it must be value of one-way delay
   observed over a relatively long enough for an opportunity to observe period (e.g., tens of minutes),
   whereas the minimum OWD with zero individual queuing delay along value is taken to be the path,
   difference between one-way delay and sufficiently
short so base delay.

   The individual sample values of queuing delay should be further
   filtered against various non-congestion-induced noise, such as spikes
   due to timely reflect "true" changes in processing "hiccup" at the network nodes.  Current
   implementation employs a 15-tab minimum OWD introduced
by route changes and other rare events. filter over per-packet
   queuing delay estimates.

5.1.2.  Estimation of packet loss/marking ratio

   The potential drawback receiver detects packet losses via gaps in relying on relative OWD as the congestion
signal is that when multiple flows share the same bottleneck, the flow RTP sequence
   numbers of received packets.  Packets arriving late at out-of-order are
   discarded, and count towards losses.  The instantaneous packet loss
   ratio p_inst is estimated as the network experiencing a non-empty queue may
mistakenly consider ratio between the standing queuing delay as part number of missing
   packets over the fixed path
propagation delay. This will lead to slightly unfair bandwidth sharing
among the flows.

Alternatively, one could move number of total transmitted packets within the per-packet statistical handling
   recent observation window LOGWIN.  The packet loss ratio p_loss is
   obtained after exponential smoothing:

       p_loss = ALPHA*p_inst + (1-ALPHA)*p_loss.   (10)

   The filtered result is reported back to the sender instead and use RTT in lieu of OWD, assuming that per-packet ACKs
are available. The main drawback as the observed
   packet loss ratio p_loss.

   Estimation of this latter approach packet marking ratio p_mark follows the same procedure
   as above.  It is assumed that ECN marking information at the
scheme will IP
   header can be confused by congestion in passed to the reverse direction.

Note that transport layer by the choice receiving
   endpoint.

5.1.3.  Estimation of either delay metric (relative OWD vs. RTT)
involves no change in receiving rate

   It is fairly straighforward to estimate the proposed receiving rate adaptation algorithm at r_recv.
   NADA maintains a recent observation window with time span of LOGWIN,
   and simply divides the
sender. Therefore, comparing total size of packets arriving during that
   window over the pros and cons regarding which delay
metric to adopt can be kept time span.  The receiving rate (r_recv) is included
   as an orthogonal direction part of
investigation.

5. NADA Sender Behavior the feedback report.

5.2.  Sender-Side Operation

   Figure 1 4 provides a detailed view of the NADA sender.  Upon receipt
   of an RTCP feedback report from the receiver, the NADA sender updates its calculation
of
   calculates the reference rate R_n. r_n as specified in Section 4.3.  It
   further adjusts both the target rate for the live video encoder R_v r_vin
   and the sending rate R_s r_send over the network based on the updated
   value of R_n, as well as r_n and rate shaping buffer occupancy buffer_len.

   The NADA sender behavior stays the size same in the presence of all types
   of congestion indicators: delay, loss, and ECN marking.  This unified
   approach allows a graceful transition of the rate
shaping buffer.

In scheme as the following, we describe these modules in further detail, network
   shifts dynamically between light and
explain how they interact with each other.

                    --------------------
                    |                  | heavy congestion levels.

                      +----------------+
                      |  Reference Rate  Calculate     | <---- RTCP report
                      |  Calculator      |
                    |                  |
                    --------------------
                            |
                            | R_n
                            |
                -------------------------- Reference Rate |
                      -----------------+
                              | r_n
                 +------------+-------------+
                 |                          |
              \ /                        \ /
    --------------------           -----------------
                \|/                        \|/
         +-----------------+           +---------------+
         | Calculate Video |           |   Calculate   |
         |  Video  Target    |           | Sending Rate    |           | Sending Rate Calculator |           | Calculator    |
    |                  |           |  |
    --------------------           -----------------
         +-----------------+           +---------------+
             |        /|\                 /|\      |
    R_v|
       r_vin |         |                   |       |         -----------------------       |
       |                     |                 | R_s
    ------------             |L_s              |
    |
            \|/        +-------------------+       |
         +----------+          | buffer_len        |  r_send
         |  Video   |  R_o    -------------- r_vout  -----------+        \|/
         |  Encoder |---------->   | | | | | --------------->
    |          |              | | | | |     video |-------->   |||||||||=================>
         +----------+         -----------+    RTP packets
    ------------         --------------
                             Rate Shaping Buffer

                      Figure 1 4: NADA Sender Structure

5.1 Reference rate calculation

5.2.1.  Rate shaping buffer

   The operation of the live video encoder is out of the scope of the
   design for the congestion control scheme in NADA.  Instead, its
   behavior is treated as a black box.

   A rate shaping buffer is employed to absorb any instantaneous
   mismatch between encoder rate output r_vout and regulated sending
   rate r_send.  Its current level of occupancy is measured in bytes and
   is denoted as buffer_len.

   A large rate shaping buffer contributes to higher end-to-end delay,
   which may harm the performance of real-time media communications.
   Therefore, the sender initializes has a strong incentive to prevent the rate
   shaping buffer from building up.  The mechanisms adopted are:

   o  To deplete the rate shaping buffer faster by increasing the
      sending rate r_send; and

   o  To limit incoming packets of the rate shaping buffer by reducing
      the video encoder target rate r_vin.

5.2.2.  Adjusting video target rate and sending rate

   The target rate for the live video encoder deviates from the network
   congestion control rate r_n based on the level of occupancy in the
   rate shaping buffer:

       r_vin = r_n - BETA_V*8*buffer_len*FPS.     (11)

   The actual sending rate r_send is regulated in a similar fashion:

       r_send = r_n + BETA_S*8*buffer_len*FPS.    (12)

   In (11) and (12), the first term indicates the reference rate R_n as R-min by default, or to
a value specified by calculated from
   network congestion feedback alone.  The second term indicates the upper-layer application. [Editor's note: should
proper choice
   influence of starting the rate value be within shaping buffer.  A large rate shaping buffer
   nudges the scope of encoder target rate slightly below -- and the CC
solution? ]

The reference sending rate R_n is calculated based on receiver feedback
information regarding queuing delay d_tilde_n, composite congestion
signal x_n, its derivative x'_n, as well as
   slightly above -- the receiving reference rate R_r. The
sender switches between two modes of operation:

        * Accelerated ramp up: if r_n.

   Intuitively, the reported queuing delay is close to
        zero and both values amount of x_n and x'_n are close extra rate offset needed to zero,
        indicating empty queues along the path of completely
   drain the flow and,
        consequently, underutilized network bandwidth; or

        * Gradual rate update: in all other conditions, whereby shaping buffer within the
        receiver reports on duration of a standing or increasing/decreasing queue
        and/or composite congestion signal.

5.1.1 Accelerated ramp up

In single video
   frame is given by 8*buffer_len*FPS, where FPS stands for the absence frame
   rate of a non-zero congestion signal the video.  The scaling parameters BETA_V and BETA_S can be
   tuned to guide balance between the sending competing goals of maintaining a small
   rate
calculation, shaping buffer and deviating the sender needs to ramp up its estimated bandwidth as
quickly as possible without introducing excessive queuing delay. Ideally system from the flow should inflict no more than T_th milliseconds reference rate
   point.

6.  Discussions and Further Investigations

6.1.  Choice of queuing delay
at the bottleneck during metrics

   The current design works with relative one-way-delay (OWD) as the ramp-up process. A typical
   main indication of congestion.  The value of T_th the relative OWD is
50ms.

Note
   obtained by maintaining the minimum value of observed OWD over a
   relatively long time horizon and subtract that out from the observed
   absolute OWD value.  Such an approach cancels out the fixed
   difference between the sender will be aware of any queuing delay introduced and receiver clocks.  It has been
   widely adopted by
its rate increase after at least one round-trip time. In addition, other delay-based congestion control approaches
   such as [RFC6817].  As discussed in [RFC6817], the
bottleneck bandwidth C is greater than or equal time horizon for
   tracking the minimum OWD needs to be chosen with care: it must be
   long enough for an opportunity to observe the receive rate R_r
reported from minimum OWD with zero
   queuing delay along the most recent "no congestion" feedback message. The rate
R_n is updated as follows:

                              T_th
    gamma = min [gamma_0, ---------------]      (3)
                           RTT_0+delta_0

    R_n  =  (1+gamma) R_r                     (4)

In (3) path, and (4), the multiplier gamma for rate increase is upper-bounded
by a fixed ratio gamma_0 (e.g., 20%), as well sufficiently short so as a ratio which depends to timely
   reflect "true" changes in minimum OWD introduced by route changes and
   other rare events.

   The potential drawback in relying on T_th, base RTT relative OWD as measured during the non-congested phase, and target
ACK interval delta_0.  The rationale behind this congestion
   signal is that when multiple flows share the rate
increase multiplier should decrease with the delay in same bottleneck, the feedback
control loop, and that RTT_0 + delta_0 provides a worst-case estimate of
feedback control delay when
   flow arriving late at the network is not congested.

5.1.2. Gradual rate update

When the receiver reports indicate experiencing a non-empty queue may
   mistakenly consider the standing congestion level, NADA
operates in gradual update mode, and calculates its reference rate as:

                   kappa * delta_s
    R_n  <-- R_n + ---------------- * (theta-(R_n-R_min)*x_hat) (5)
                      tau_o^2

where

    theta = w*(R_max - R_min)*x_ref.    (6)

    x_hat = x_n + eta*tau_o* x'_n       (7)

In (5), delta_s refers queuing delay as part of the fixed
   path propagation delay.  This will lead to slightly unfair bandwidth
   sharing among the time interval between current flows.

   Alternatively, one could move the per-packet statistical handling to
   the sender instead and previous
rate updates. Note use relative round-trip-time (RTT) in lieu of
   relative OWD, assuming that delta_s per-packet acknowledgements are
   available.  The main drawback of RTT-based approach is the same as the RTCP report interval
at the receiver (see delta from (2)) when the backward path is un-
congested.

In (6), R_min and R_max denote noise in
   the content-dependent rate range measured delay in the
encoder can produce. The weighting factor reflecting a flow's priority
is w. The reference congestion signal x_ref is chosen so reverse direction.

   Note that the
maximum rate of R_max can be achieved when x_hat = w*x_ref.

Proper choice of the scaling parameters eta and kappa either delay metric (relative OWD vs. RTT)
   involves no change in (5) the proposed rate adaptation algorithm.
   Therefore, comparing the pros and (7) cons regarding which delay metric
   to adopt can
ensure system stability so long be kept as an orthogonal direction of investigation.

6.2.  Method for delay, loss, and marking ratio estimation

   Like other delay-based congestion control schemes, performance of
   NADA depends on the RTT falls below the upper bound accuracy of tau_o. its delay measurement and estimation
   module.  Appendix A in [RFC6817] provides an extensive discussion on
   this aspect.

   The current recommended default value practice of tau_o is chosen as 500ms. simply applying a 15-tab minimum
   filter suffices in guarding against processing delay outliers
   observed in wired connections.  For both modes wireless connections with a
   higher packet delay variation (PDV), more sophisticated techniques on
   de-noising, outlier rejection, and trend analysis may be needed.

   More sophisticated methods in packet loss ratio calculation, such as
   that adopted by [Floyd-CCR00], will likely be beneficial.  These
   alternatives are currently under investigation.

6.3.  Impact of operations, parameter values

   In the final reference gradual rate R_n is clipped
within update mode, the range of [R_min, R_max]. Note also that parameter TAU indicates the sender does not
need any explicit knowledge
   upper bound of round-trip-time (RTT) in feedback control loop.
   Typically, the management scheme inside the network.
Rather, it reacts observed feedback interval delta is close to the aggregation
   target feedback interval DELTA, and the relative ratio of all forms delta/TAU
   versus ETA dictates the relative strength of congestion
indications (delay, loss, and explicit markings) via influence from the composite
   aggregate congestion signals x_n and x'_n from signal offset term (x_offset) versus its recent
   change (x_diff), respectively.  These two terms are analogous to the receiver
   integral and proportional terms in a coherent manner.

5.2 Video encoder rate control proportional-integral (PI)
   controller.  The video encoder rate control procedure has the following
characteristics:

    * Rate changes can happen only at large intervals, on the order recommended choice of
    seconds.

    * The encoder output rate may fluctuate around TAU=500ms, DELTA=100ms and ETA
   = 2.0 corresponds to a relative ratio of 1:10 between the target rate R_v.

    * The encoder output rate is further constrained by video content
    complexity. The range gains of
   the final rate output is [R_min, R_max].
    Note that it is content-dependent integral and may vary over time.

The operation of proportional terms.  Consequently, the live video encoder rate
   adaptation is out of the scope of mostly driven by the
design for change in the congestion control scheme in NADA. Instead, its behavior
is treated as signal
   with a black box.

5.3 Rate shaping buffer

A rate shaping buffer is employed long-term shift towards its equilibrium value driven by the
   offset term.  Finally, the scaling parameter KAPPA determines the
   overall speed of the adaptation and needs to absorb any instantaneous mismatch strike a balance between encoder rate output R_o
   responsiveness and regulated sending rate R_s. stability.

   The size choice of the buffer evolves from time t-tau target feedback interval DELTA needs to time t as:

              L_s(t) = max [0, L_s(t-tau)+(R_o-R_s)*tau]. strike the
   right balance between timely feedback and low RTCP feedback message
   counts.  A large rate shaping buffer contributes target feedback interval of DELTA=100ms is recommended,
   corresponding to higher end-to-end delay,
which may harm a feedback bandwidth of 16Kbps with 200 bytes per
   feedback message --- less than 0.1% overhead for a 1 Mbps flow.
   Furthermore, both simulation studies and frequency-domain analysis
   have established that a feedback interval below 250ms will not break
   up the performance feedback control loop of real-time media communications.
Therefore, the sender has a strong incentive to constrain NADA congestion control.

   In calculating the size non-linear warping of delay in (1), the shaping buffer. current
   design uses fixed values of QTH and QMAX.  It can either deplete it faster by increasing is possible to adapt
   the
sending rate R_s, or limit its growth by reducing value of both based on past observations of queuing delay in the target rate for
   presence of packet losses.

   In calculating the video encoder rate control R_v.

5.4 Adjusting video target rate aggregate congestion signal x_n, the choice of
   DMARK and sending rate

The target rate for DLOSS influence the live video encoder is updated based on both steady-state packet loss/marking ratio
   experienced by the
reference rate R_n flow at a given available bandwidth.  Higher
   values of DMARK and DLOSS result in lower steady-state loss/marking
   ratios, but are more susceptible to the rate shaping buffer size L_s, as follows:

                               L_s
        R_v = R_n - beta_v * -------.       (8)
                              tau_v

Similarly, the outgoing rate is regulated based on both impact of individual packet
   loss/marking events.  While the reference
rate R_n value of DMARK and the rate shaping buffer size L_s, such that:

                               L_s
        R_s = R_n + beta_s * -------.       (9)
                              tau_v

In (8) DLOSS are fixed
   and (9), the first term indicates predetermined in the rate calculated from
network congestion feedback alone. The second term indicates current design, a scheme for automatically
   tuning these values based on desired bandwidth sharing behavior in
   the
influence presence of other competing loss-based flows (e.g., loss-based
   TCP) is under investigation.

   [Editor's note: Choice of start value: is this in scope of congestion
   control, or should this be decided by the rate shaping buffer. A large rate shaping buffer nudges application?]

6.4.  Sender-based vs. receiver-based calculation

   In the encoder target rate slightly below -- and current design, the sending rate slightly
above -- aggregated congestion signal x_n is
   calculated at the reference rate R_n.

Intuitively, receiver, keeping the amount of extra rate offset needed to sender operation completely drain
the rate shaping buffer within
   independent of the same time frame form of encoder rate
adaptation tau_v is given by L_s/tau_v. The scaling parameters beta_v
and beta_s actual network congestion indications
   (delay, loss, or marking).  Alternatively, one can be tuned to balance between move the competing goals logics of
maintaining a small rate shaping buffer
   (1) and deviating (2) to the system from sender.  Such an approach requires slightly higher
   overhead in the reference feedback messages, which should contain individual
   fields on queuing delay (d_n), packet loss ratio (p_loss), packet
   marking ratio (p_mark), receiving rate point.

6. (r_recv), and recommended rate
   adaptation mode (rmode).

6.5.  Incremental Deployment deployment

   One nice property of NADA is the consistent video endpoint behavior
   irrespective of network node variations.  This facilitates gradual,
   incremental adoption of the scheme.

   To start off with, the encoder proposed congestion control mechanism can be
   implemented without any explicit support from the network, and relies
   solely on observed one-way delay measurements and packet loss ratios
   as implicit congestion signals.

   When ECN is enabled at the network nodes with RED-based marking, the
   receiver can fold its observations of ECN markings into the
   calculation of the equivalent delay.  The sender can react to these
   explicit congestion signals without any modification.

   Ultimately, networks equipped with proactive marking based on token
   bucket level metering can reap the additional benefits of zero
   standing queues and lower end-to-end delay and work seamlessly with
   existing senders and receivers.

7.  Implementation Status

   The NADA scheme has been implemented in the ns-2 [ns-2] and [ns-3] simulation platform
[ns2].
   platforms.  Extensive ns-2 simulation evaluations of an earlier
   version of the draft are documented in [Zhu-PV13].  Evaluation
   results of the current draft over several test cases in [I-D.draft-sarker-rmcat-eval-test]
   [I-D.ietf-rmcat-eval-test] have been presented at recent IETF
   meetings [IETF-90][IETF-91].

   The scheme has also been implemented and evaluated in a lab setting
   as described in [IETF-90].  Preliminary evaluation results of NADA in
   single-flow and multi-flow scenarios have been presented in
   [IETF-91].

8.  IANA Considerations

There are

   This document makes no actions for request of IANA.

9.  Acknowledgements

   The authors would like to thank Randell Jesup, Luca De Cicco, Piers
   O'Hanlon, Ingemar Johansson, Stefan Holmer, Cesar Ilharco Magalhaes,
   Safiqul Islam, Mirja Kuhlewind, and Karen Elisabeth Egede Nielsen for
   their valuable questions and comments on earlier versions of this
   draft.

10.  References

9.1

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/
              RFC2119, March 1997. 1997,
              <http://www.rfc-editor.org/info/rfc2119>.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP", RFC
              3168, DOI 10.17487/RFC3168, September 2001. 2001,
              <http://www.rfc-editor.org/info/rfc3168>.

   [RFC3550]  Schulzrinne, H., Casner, S., Frederick, R., and V.
              Jacobson, "RTP: A Transport Protocol for Real-Time
              Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
              July 2003.

9.2  Informative References

   [RFC3168]  Ramakrishnan, K., Floyd, S., 2003, <http://www.rfc-editor.org/info/rfc3550>.

   [I-D.ietf-rmcat-eval-criteria]
              Singh, V. and D. Black, "The Addition
              of Explicit J. Ott, "Evaluating Congestion Notification (ECN) to IP",
              RFC 3168, Control for
              Interactive Real-time Media", draft-ietf-rmcat-eval-
              criteria-03 (work in progress), March 2015.

   [I-D.ietf-rmcat-eval-test]
              Sarker, Z., Singh, V., Zhu, X., and M. Ramalho, "Test
              Cases for Evaluating RMCAT Proposals", draft-ietf-rmcat-
              eval-test-02 (work in progress), September 2001. 2015.

   [I-D.ietf-rmcat-cc-requirements]
              Jesup, R. and Z. Sarker, "Congestion Control Requirements
              for Interactive Real-Time Media", draft-ietf-rmcat-cc-
              requirements-09 (work in progress), December 2014.

10.2.  Informative References

   [RFC2309]  Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
              S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
              Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
              S., Wroclawski, J., and L. Zhang, "Recommendations on
              Queue Management and Congestion Avoidance in the
              Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998. 1998,
              <http://www.rfc-editor.org/info/rfc2309>.

   [RFC5348]  Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
              Friendly Rate Control (TFRC): Protocol Specification", RFC
              5348, DOI 10.17487/RFC5348, September 2008,
              <http://www.rfc-editor.org/info/rfc5348>.

   [RFC6660]  Briscoe, B., Moncaster, T., and M. Menth, "Encoding Three
              Pre-Congestion Notification (PCN) States in the IP Header
              Using a Single Diffserv Codepoint (DSCP)", RFC 6660, DOI
              10.17487/RFC6660, July 2012,
              <http://www.rfc-editor.org/info/rfc6660>.

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind, M.,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012 2012,
              <http://www.rfc-editor.org/info/rfc6817>.

   [Floyd-CCR00]
              Floyd, S., Handley, M., Padhye, J., and J. Widmer, J.,
              "Equation-based Congestion Control for Unicast
              Applications", ACM SIGCOMM Computer Communications Review, Review
              vol. 30. 30, no. 4., 4, pp. 43-56, October 2000.

   [Budzisz-TON11]
              Budzisz, L. et al., L., Stanojevic, R., Schlote, A., Baker, F., and
              R. Shorten, "On the Fair Coexistence of Loss- and Delay-Based Delay-
              Based TCP", IEEE/ACM Transactions on
              Networking, Networking vol. 19,
              no. 6, pp. 1811-1824, December 2011.

   [ns2] "The Network Simulator - ns-2", http://www.isi.edu/nsnam/ns/

   [Zhu-PV13]
              Zhu, X. and R. Pan, R., "NADA: A Unified Congestion Control
              Scheme for Low-Latency Interactive Video", in Proc. IEEE
              International Packet Video Workshop (PV'13). (PV'13) San Jose, CA,
              USA.
              USA, December 2013.

   [I-D.draft-sarker-rmcat-eval-test] Sarker, Z., Singh, V.,

   [ns-2]     "The Network Simulator - ns-2",
              <http://www.isi.edu/nsnam/ns/>.

   [ns-3]     "The Network Simulator - ns-3", <https://www.nsnam.org/>.

   [IETF-90]  Zhu, X.,
              and Ramalho, M., "Test Cases for Evaluating RMCAT
              Proposals", draft-sarker-rmcat-eval-test-01 (work in
              progress), June 2014.

   [IETF-90] Zhu, X. et al., Ganzhorn, C., Jones, P., and R. Pan,
              "NADA Update: Algorithm, Implementation, and Test Case
              Evalua6on Results", presented at IETF 90,
              https://tools.ietf.org/agenda/90/slides/slides-90-rmcat-
              6.pdf July 2014,
              <https://tools.ietf.org/agenda/90/slides/slides-90-rmcat-
              6.pdf>.

   [IETF-91]  Zhu, X. et al., X., Pan, R., Ramalho, M., Mena, S., Ganzhorn, C.,
              Jones, P., and S. D'Aronco, "NADA Algorithm Update and
              Test Case Evaluations", presented at IETF 91 Interium,
              https://datatracker.ietf.org/meeting/91/agenda/rmcat/ November 2014,
              <http://www.ietf.org/proceedings/interim/2014/11/09/rmcat/
              slides/slides-interim-2014-rmcat-1-2.pdf>.

Appendix A.  Network Node Operations

   NADA can work with different network queue management schemes and
   does not assume any specific network node operation.  As an example,
   this appendix describes three
              variations variants of queue management behavior
   at the network node, leading to either implicit or explicit
   congestion signals.

   In all three flavors described below, the network queue operates with
   the simple first-in-first-out (FIFO) principle.  There is no need to
   maintain per-flow state.
              Such a simple design ensures that the  The system can scale easily with a large
   number of video flows and at high link capacity.

              NADA sender behavior stays the same in the presence of all
              types of congestion indicators: delay, loss, ECN marking
              due to either RED/ECN or PCN algorithms. This unified
              approach allows a graceful transition of the scheme as the
              network shifts dynamically between light and heavy
              congestion levels.

A.1

A.1.  Default behavior of drop tail queues

   In a conventional network with drop tail or RED queues, congestion is
   inferred from the estimation of end-to-end delay and/or packet loss.
   Packet drops at the queue are detected at the receiver, and
   contributes to the calculation of the equivalent delay aggregated congestion signal
   x_n.  No special action is required at network node.

A.2

A.2.  RED-based ECN marking

   In this mode, the network node randomly marks the ECN field in the IP
   packet header following the Random Early Detection (RED) algorithm
   [RFC2309].  Calculation of the marking probability involves the
   following steps:

    * upon

       on packet arrival, arrival:
           update smoothed queue size q_avg as:
               q_avg = alpha*q w*q + (1-alpha)*q_avg.

    The smoothing parameter alpha is a value between 0 and 1. A value of
    alpha=1 corresponds to performing no smoothing at all.

    * (1-w)*q_avg.

           calculate marking probability p as:

        p =

              / 0,                    if q < q_lo;
              |
              |        q_avg - q_lo
        p =
          p= <  p_max*--------------, if q_lo <= q < q_hi;
              |         q_hi - q_lo
              |
              \ p = 1,                if q >= q_hi.

   Here, q_lo and q_hi corresponds to the low and high thresholds of
   queue occupancy.  The maximum marking probability is p_max.

   The ECN markings events will contribute to the calculation of an
   equivalent delay x_n at the receiver.  No changes are required at the
   sender.

A.3 PCN marking

As a more advanced feature, we also envisage

A.3.  Random Early Marking with Virtual Queues

   Advanced network nodes which may support
PCN random early marking based on virtual queues. In such a case, the marking
probability of the ECN
   token bucket algorithm originally designed for Pre-Congestion
   Notification (PCN) [RFC6660].  The early congestion notification
   (ECN) bit in the IP packet header of packets are marked randomly.  The
   marking probability is calculated based on a token-bucket algorithm
   originally designed for the Pre-Congestion Notification (PCN)
   [RFC6660].  The target link utilization is set as
follows: 90%; the marking
   probability is designed to grow linearly with the token bucket size
   when it varies between 1/3 and 2/3 of the full token bucket limit.

   * upon packet arrival, meter packet against token bucket (r,b);

   * update token level b_tk;

   * calculate the marking probability as:

        p =

            / 0,                     if b-b_tk < b_lo;
            |
            |          b-b_tk-b_lo
       p = <  p_max* --------------, if b_lo<= b-b_tk <b_hi;
            |           b_hi-b_lo

        p =
            |
            \ 1,                     if b-b_tk>=b_hi.

   Here, the token bucket lower and upper limits are denoted by b_lo and
   b_hi, respectively.  The parameter b indicates the size of the token
   bucket.  The parameter r is chosen as r=gamma*C, where gamma<1 is to be below capacity, resulting in
   slight under-utilization of the
target utilization ratio and C designates link capacity. link.  The maximum marking
   probability is p_max.

   The ECN markings events will contribute to the calculation of an
   equivalent delay x_n at the receiver.  No changes are required at the
   sender.  The virtual queuing mechanism from the PCN PCN-based marking
   algorithm will lead to additional benefits such as zero standing
   queues.

Authors' Addresses

   Xiaoqing Zhu
   Cisco Systems, Systems
   12515 Research Blvd., Building 4
   Austin, TX 78759,  78759
   USA

   Email: xiaoqzhu@cisco.com

   Rong Pan
   Cisco Systems
510 McCarthy Blvd,
Milpitas,
   3625 Cisco Way
   San Jose, CA 95134,  95134
   USA

   Email: ropan@cisco.com

   Michael A. Ramalho
6310 Watercrest Way Unit 203
Lakewood Ranch, FL, 34202,
   Cisco Systems, Inc.
   8000 Hawkins Road
   Sarasota, FL  34241
   USA

   Phone: +1 919 476 2038
   Email: mramalho@cisco.com
   Sergio Mena de la Cruz
   Cisco Systems
   EPFL, Quartier de l'Innovation, Batiment E
   Ecublens, Vaud 1015,  1015
   Switzerland

   Email: semena@cisco.com

Charles Ganzhorn
7900 International Drive
International Plaza, Suite 400
Bloomington, MN 55425, USA
Email: charles.ganzhorn@gmail.com

   Paul E. Jones
   Cisco Systems
   7025 Kit Creek Rd.
   Research Triangle Park, NC 27709,  27709
   USA

   Email: paulej@packetizer.com

   Jiantao Fu
   Cisco Systems
   707 Tasman Drive
   Milpitas, CA  95035
   USA

   Email: jianfu@cisco.com

   Stefano D'Aronco
   Ecole Polytechnique Federale de Lausanne
   EPFL STI IEL LTS4 LTS4, ELD 220 (Batiment ELD), Station 11
   Lausanne  CH-1015 Lausanne,
   Switzerland

   Email: stefano.daronco@epfl.ch

   Charles Ganzhorn
   7900 International Drive, International Plaza, Suite 400
   Bloomington, MN  55425
   USA

   Email: charles.ganzhorn@gmail.com