Network Working Group                                             X. Zhu
Internet-Draft                                                    R. Pan
Intended status: Experimental                                 M. Ramalho
Expires: April 1, 2018                                           S. Mena
                                                                P. Jones
                                                                   J. Fu
                                                           Cisco Systems
                                                             S. D'Aronco
                                                      September 28, 2017

     NADA: A Unified Congestion Control Scheme for Real-Time Media


   This document describes NADA (network-assisted dynamic adaptation), a
   novel congestion control scheme for interactive real-time media
   applications, such as video conferencing.  In the proposed scheme,
   the sender regulates its sending rate based on either implicit or
   explicit congestion signaling, in a 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 such markings, by reacting to queuing delays and packet
   losses instead.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on April 1, 2018.

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Copyright Notice

   Copyright (c) 2017 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   ( in effect on the date of
   publication of this document.  Please review these documents
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  System Overview . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Core Congestion Control Algorithm . . . . . . . . . . . . . .   5
     4.1.  Mathematical Notations  . . . . . . . . . . . . . . . . .   5
     4.2.  Receiver-Side Algorithm . . . . . . . . . . . . . . . . .   8
     4.3.  Sender-Side Algorithm . . . . . . . . . . . . . . . . . .  10
   5.  Practical Implementation of NADA  . . . . . . . . . . . . . .  12
     5.1.  Receiver-Side Operation . . . . . . . . . . . . . . . . .  12
       5.1.1.  Estimation of one-way delay and queuing delay . . . .  12
       5.1.2.  Estimation of packet loss/marking ratio . . . . . . .  12
       5.1.3.  Estimation of receiving rate  . . . . . . . . . . . .  13
     5.2.  Sender-Side Operation . . . . . . . . . . . . . . . . . .  13
       5.2.1.  Rate shaping buffer . . . . . . . . . . . . . . . . .  14
       5.2.2.  Adjusting video target rate and sending rate  . . . .  15
     5.3.  Feedback Message Requirements . . . . . . . . . . . . . .  15
   6.  Discussions and Further Investigations  . . . . . . . . . . .  16
     6.1.  Choice of delay metrics . . . . . . . . . . . . . . . . .  16
     6.2.  Method for delay, loss, and marking ratio estimation  . .  16
     6.3.  Impact of parameter values  . . . . . . . . . . . . . . .  17
     6.4.  Sender-based vs. receiver-based calculation . . . . . . .  18
     6.5.  Incremental deployment  . . . . . . . . . . . . . . . . .  18
   7.  Implementation Status . . . . . . . . . . . . . . . . . . . .  18
   8.  Suggested Experiments . . . . . . . . . . . . . . . . . . . .  19
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  19
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  19
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  20
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  20
     11.2.  Informative References . . . . . . . . . . . . . . . . .  21
   Appendix A.  Network Node Operations  . . . . . . . . . . . . . .  22
     A.1.  Default behavior of drop tail queues  . . . . . . . . . .  22

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     A.2.  RED-based ECN marking . . . . . . . . . . . . . . . . . .  22
     A.3.  Random Early Marking with Virtual Queues  . . . . . . . .  23
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

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.  The requirements
   for the congestion control algorithm are outlined in

   This document describes 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.  Such a
   unified sender behavior distinguishes NADA from other congestion
   control schemes for real-time media.  In addition, its core
   congestion control algorithm is designed to guarantee stability for
   path round-trip-times (RTTs) below a prescribed bound (e.g., 250ms
   with default parameter choices).  It further supports weighted
   bandwidth sharing among competing video flows with different
   priorities.  The signaling mechanism consists of standard RTP
   timestamp [RFC3550] and RTCP feedback reports with non-standard

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described [RFC2119].

3.  System Overview

   Figure 1 shows the end-to-end system for real-time media transport
   that NADA operates in.  Note that there also exist network nodes
   along the reverse (potentially uncongested) path that the RTCP
   feedback reports traverse.  Those network nodes are not shown in the
   figure for sake of abrevity.

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     +---------+  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 encodes raw
      media (audio and video) frames into compressed bitstream which is
      later packetized into RTP packets.  As discussed in
      [I-D.ietf-rmcat-video-traffic-model], the actual output rate from
      the encoder r_vout may fluctuate around the target r_vin.
      Furthermore, it is possible that the encoder can only react to bit
      rate changes at rather coarse time intervals, e.g., once every 0.5

   o  RTP sender: responsible for calculating the NADA reference rate
      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_vin, and for regulating the actual
      sending rate r_send accordingly.  The RTP sender also generates a
      sending timestamp for each outgoing packet.

   o  RTP receiver: responsible for measuring and estimating end-to-end
      delay (based on sender timestamp), packet loss (based on RTP
      sequence number), ECN marking ratios (based on [RFC6679]), and
      receiving rate (r_recv) of the flow.  It calculates the aggregated
      congestion signal (x_curr) that accounts for queuing delay, ECN
      markings, and packet losses.  The receiver also determines the
      mode for sender rate adaptation (rmode) based on whether the flow
      has encountered any standing non-zero congestion.  The receiver
      sends periodic RTCP reports back to the sender, containing values
      of x_curr, rmode, and r_recv.

   o  Network 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 control for
      the design of NADA.

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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 sender reacts to the collection of network congestion indicators
   in the form of an aggregated congestion signal, and operates in one
   of two modes:

   o  Accelerated ramp-up: when the bottleneck is deemed to be
      underutilized, the rate increases multiplicatively with respect to
      the rate of previously successful transmissions.  The rate
      increase mutliplier (gamma) is calculated based on observed round-
      trip-time and target feedback interval, so as to limit self-
      inflicted queuing delay.

   o  Gradual rate update: in the presence of non-zero aggregate
      congestion signal, the sending rate is adjusted in reaction to
      both its value (x_curr) and its change in value (x_diff).

   This section introduces the list of mathematical notations and
   describes the core congestion control algorithm at the sender and
   receiver, respectively.  Additional details on recommended practical
   implementations are described in Section 5.1 and Section 5.2.

4.1.  Mathematical Notations

   This section summarizes the list of variables and parameters used in
   the NADA algorithm.

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     | Notation     | Variable Name                                   |
     | t_curr       | Current timestamp                               |
     | t_last       | Last time sending/receiving a feedback          |
     | delta        | Observed interval between current and previous  |
     |              | feedback reports: delta = t_curr-t_last         |
     | r_ref        | 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                        |
     | d_fwd        | Measured and filtered one-way delay             |
     | d_queue      | Estimated queueing delay                        |
     | d_tilde      | Equivalent delay after non-linear warping       |
     | p_mark       | Estimated packet ECN marking ratio              |
     | p_loss       | Estimated packet loss ratio                     |
     | x_curr       | Aggregate congestion 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_curr - x_prev    |
     | rmode        | Rate update mode: (0 = accelerated ramp-up;     |
     |              | 1 = gradual update)                             |
     | gamma        | Rate increase multiplier in accelerated ramp-up |
     |              | mode                                            |
     | tloss_int    | Measured average loss interval                  |
     | tloss_exp    | Time window for recently observed losses        |
     | 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 the flow   |    1.0
    | RMIN         | Minimum rate of application      |    150 Kbps    |
    |              | supported by media encoder       |                |
    | RMAX         | Maximum rate of application      |    1.5 Mbps    |
    |              | supported by media encoder       |                |
    | XREF         | Reference congestion level       |    10ms        |
    | KAPPA        | Scaling parameter for gradual    |    0.5         |
    |              | rate update calculation          |                |
    | ETA          | Scaling parameter for gradual    |    2.0         |
    |              | rate update calculation          |                |

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    | TAU          | Upper bound of RTT in gradual    |    500ms       |
    |              | rate update calculation          |                |
    | DELTA        | Target feedback interval         |    100ms       |
    | DFILT        | Bound on filtering delay         |    120ms       |
    | LOGWIN       | Observation window in time for   |    500ms       |
    |              | calculating packet summary       |                |
    |              | statistics at receiver           |                |
    | QEPS         | Threshold for determining queuing|     10ms       |
    |              | delay build up at receiver       |                |
    | GAMMA_MAX    | Upper bound on rate increase     |     50%        |
    |              | ratio for accelerated ramp-up    |                |
    | QBOUND       | Upper bound on self-inflicted    |    50ms        |
    |              | queuing delay during ramp up     |                |
    | MULTILOSS    | Multiplier for self-scaling the  |     7.         |
    |              | recent observation time window   |                |
    |              | (tloss_exp) based on measured    |                |
    |              | average loss interval (tloss_int)|                |
    | QTH          | Delay threshold for invoking     |     50ms       |
    |              | non-linear warping               |                |
    | LAMBDA       | Scaling parameter in the         |     0.5        |
    |              | exponent of non-linear warping   |                |
    | PLRREF       | Reference packet loss ratio      |    0.01        |
    | PMRREF       | Reference packet marking ratio   |    0.01        |
    | DLOSS        | Reference delay penalty for loss |    10ms        |
    |              | when packet loss ratio is at     |                |
    |              | PLRREF                           |                |
    | DMARK        | Reference delay penalty for ECN  |     2ms        |
    |              | marking when packet marking      |                |
    |              | is at PMRREF                     |                |
    | FPS          | Frame rate of 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 exponential  |    0.1         |
    |              | smoothing of packet loss and     |                |
    |              | marking ratios                   |

                  Figure 3: List of algorithm parameters.

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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 t_curr as current time

  On receiving a media packet:
    obtain current timestamp t_curr from system clock
    obtain from packet 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_queue = d_fwd - d_base
    update packet loss ratio estimate p_loss
    update packet marking ratio estimate p_mark
    update measurement of receiving rate r_recv

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

   In order for a delay-based flow to hold its ground when competing
   against loss-based 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 or induced by other 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.

   If packet losses are observed within the previous time window of
   tloss_exp, the estimated queuing delay follows a non-linear warping:

              / d_queue,                   if d_queue<QTH;
   d_tilde = <                                           (1)
              |                  (d_queue-QTH)
              \ QTH exp(-LAMBDA ---------------) , otherwise.

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   In (1), the queuing delay value is unchanged when it is below the
   first threshold QTH; otherwise it is scaled down following a non-
   linear curve.  This non-linear warping is inspired by the delay-
   adaptive congestion windown backoff policy in [Budzisz-TON11], so as
   to "gradually nudge" the controller to operate based on loss-induced
   congestion signals when competing against loss-based flows.  The
   exact form of the non-linear function has been simplified with
   respect to [Budzisz-TON11].

   The value of tloss_exp is configured to self-scale with the average
   loss interval tloss_int with a multiplier MULTILOSS:

         tloss_exp = MULTILOSS * tloss_int.

   Estimation of the average loss interval tloss_int, in turn, follows
   Section 5.4 of the TCP Friendly Rate Control (TFRC) protocol

   In practice, it is recommended to linearly interpolate between the
   warped (d_tilde) and non-warped (d_queue) values of the queuing delay
   during the transitional period lasting for the duration of tloss_int.

   The aggregate congestion signal is:

  x_curr = d_tilde + DMARK*(p_mark/PMRREF)^2 + DLOSS*(p_loss/PLRREF)^2.  (2)

   Here, DMARK is prescribed reference delay penalty associated with ECN
   markings at the reference marking ratio of PMRREF; DLOSS is
   prescribed reference delay penalty associated with packet losses at
   the reference packet loss ratio of PLRREF.  The value of DLOSS and
   DMARK does not depend on configurations at the network node.  Since
   ECN-enabled active queue management schemes typically mark a packet
   before dropping it, the value of DLOSS SHOULD be higher than that of
   DMARK.  Furthermore, the values of DLOSS and DMARK need to be set
   consistently across all NADA flows for them to compete fairly.

   In the absence of packet marking and losses, the value of x_curr
   reduces to the observed queuing delay d_queue.  In that case the NADA
   algorithm operates in the regime of delay-based adaptation.

   Given observed per-packet delay and loss information, the receiver is
   also in a good position to determine whether the network is
   underutilized and recommend the corresponding rate adaptation mode

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   for the sender.  The criteria for operating in accelerated ramp-up
   mode are:

   o  No recent packet losses within the observation window LOGWIN; and

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

   Otherwise the algorithm operates in graduate update mode.

4.3.  Sender-Side Algorithm

   The sender-side algorithm is outlined as follows:

     on initialization:
       set r_ref = RMIN
       set rtt = 0
       set x_prev = 0
       set t_last and t_curr as current system clock time

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

   In accelerated ramp-up mode, the rate r_ref is updated as follows:

       gamma = min(GAMMA_MAX, ------------------)     (3)

       r_ref = max(r_ref, (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), target feedback interval (DELTA) and bound on filtering delay
   for calculating d_queue (DFILT).  It has a 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

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   more conservative the sender should be in increasing its rate, hence
   the smaller the rate increase multiplier.

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

       x_offset = x_curr - PRIO*XREF*RMAX/r_ref          (5)

       x_diff   = x_curr - x_prev                        (6)

                              delta    x_offset
       r_ref = r_ref - KAPPA*-------*------------*r_ref
                               TAU       TAU

                     - KAPPA*ETA*---------*r_ref         (7)

   The rate changes in proportion to the previous rate decision.  It is
   affected by two terms: offset of the aggregate congestion signal from
   its value at equilibrium (x_offset) and its change (x_diff).
   Calculation of x_offset depends on maximum rate of the flow (RMAX),
   its weight of priority (PRIO), as well as a reference congestion
   signal (XREF).  The value of XREF is chosen so that the maximum rate
   of RMAX can be achieved when the observed congestion signal level is
   below PRIO*XREF.

   At equilibrium, the aggregated congestion signal stablizes at x_curr
   = PRIO*XREF*RMAX/r_ref.  This ensures that when multiple flows share
   the same bottleneck and observe a common value of x_curr, their rates
   at equilibrium will be proportional to their respective priority
   levels (PRIO) and maximum rate (RMAX).  Values of RMIN and RMAX will
   be provided by the media codec, as specified in
   [I-D.ietf-rmcat-cc-codec-interactions].  In the absense of such
   information, NADA sender will choose a default value of 0 for RMIN,
   and 2Mbps for RMAX.

   As mentioned in the sender-side algorithm, the final rate is clipped
   within the dynamic range specified by the application:

           r_ref = min(r_ref, RMAX)          (8)

           r_ref = max(r_ref, RMIN)          (9)

   The above operations ignore many practical issues such as clock
   synchronization between sender and receiver, filtering of noise in

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   delay measurements, and base delay expiration.  These will be
   addressed in Section 5

5.  Practical Implementation of NADA

5.1.  Receiver-Side Operation

   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 of congestion indicators into the form of an
   equivalent delay and periodically reports this back to the sender.
   In addition, the receiver tracks the receiving rate of the flow and
   includes that in the feedback message.

5.1.1.  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].  Instead of relying on RTP timestamps, the NADA sender
   generates its own timestamp based on local system clock and embeds
   that information in the transport packet header.  The NADA receiver
   estimates the forward delay as having a constant base delay component
   plus a time varying queuing delay component.  The base delay is
   estimated as the minimum value of one-way delay observed over a
   relatively long period (e.g., tens of minutes), whereas the
   individual queuing delay value is taken to be the difference between
   one-way delay and base delay.  All delay estimations are based on
   sender timestamps with higher granularity than RTP timestamps.

   The individual sample values of queuing delay should be further
   filtered against various non-congestion-induced noise, such as spikes
   due to processing "hiccup" at the network nodes.  Current
   implementation employs a 15-tap minimum filter over per-packet
   queuing delay estimates.

5.1.2.  Estimation of packet loss/marking ratio

   The receiver detects packet losses via gaps in the RTP sequence
   numbers of received packets.  Packets arriving out-of-order are
   discarded, and count towards losses.  The instantaneous packet loss
   ratio p_inst is estimated as the ratio between the number of missing
   packets over the number of total transmitted packets within the
   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)

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   The filtered result is reported back to the sender as the observed
   packet loss ratio p_loss.

   Estimation of packet marking ratio p_mark follows the same procedure
   as above.  It is assumed that ECN marking information at the IP
   header can be passed to the receiving endpoint, e.g., by following
   the mechanism described in [RFC6679].

5.1.3.  Estimation of receiving rate

   It is fairly straighforward to estimate the receiving rate r_recv.
   NADA maintains a recent observation window with time span of LOGWIN,
   and simply divides the total size of packets arriving during that
   window over the time span.  The receiving rate (r_recv) is included
   as part of the feedback report.

5.2.  Sender-Side Operation

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

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

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                      |  Calculate     | <---- RTCP report
                      | Reference Rate |
                              | r_ref
                 |                          |
                \|/                        \|/
         +-----------------+           +---------------+
         | Calculate Video |           |   Calculate   |
         |  Target Rate    |           | Sending Rate  |
         +-----------------+           +---------------+
             |        /|\                 /|\      |
       r_vin |         |                   |       |
            \|/        +-------------------+       |
         +----------+          | buffer_len        |  r_send
         |  Video   | r_vout  -----------+        \|/
         |  Encoder |-------->   |||||||||=================>
         +----------+         -----------+    RTP packets
                             Rate Shaping Buffer

                      Figure 4: NADA Sender Structure

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 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.

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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_ref based on the level of occupancy in the
   rate shaping buffer:

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

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

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

   In (11) and (12), the first term indicates the rate calculated from
   network congestion feedback alone.  The second term indicates the
   influence of the rate shaping buffer.  A large rate shaping buffer
   nudges the encoder target rate slightly below -- and the sending rate
   slightly above -- the reference rate r_ref.

   Intuitively, the amount of extra rate offset needed to completely
   drain the rate shaping buffer within the duration of a single video
   frame is given by 8*buffer_len*FPS, where FPS stands for the frame
   rate of the video.  The scaling parameters BETA_V and BETA_S can be
   tuned to balance between the competing goals of maintaining a small
   rate shaping buffer and deviating from the reference rate point.

5.3.  Feedback Message Requirements

   The following list of information is required for NADA congestion
   control to function properly:

   o  Recommended rate adaptation mode (rmode): a 1-bit flag indicating
      whether the sender should operate in accelerated ramp-up mode
      (rmode=0) or gradual update mode (rmode=1).

   o  Aggregated congestion signal (x_curr): the most recently updated
      value, calculated by the receiver according to Section 4.2.  This
      information is expressed with a unit of 100 microsecond (i.e.,
      1/10 of a millisecond) in 15 bits.  This allows a maximum value of
      x_curr at approximately 3.27 second.

   o  Receiving rate (r_recv): the most recently measured receiving rate
      according to Section 5.1.3.  This information is expressed with a
      unit of bits per second (bps) in 32 bits (unsigned int).  This
      allows a maximum rate of approximately 4.3Gbps.

   The above list of information can be accommodated by 48 bits, or 6
   bytes, in total.  Choice of the feedback message interval DELTA is

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   discussed in Section 6.3 A target feedback interval of DELTA=100ms is

6.  Discussions and Further Investigations

6.1.  Choice of delay metrics

   The current design works with relative one-way-delay (OWD) as the
   main indication of congestion.  The value of the relative OWD is
   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 and receiver clocks.  It has been
   widely adopted by other delay-based congestion control approaches
   such as [RFC6817].  As discussed in [RFC6817], the time horizon for
   tracking the minimum OWD needs to be chosen with care: it must be
   long enough for an opportunity to observe the minimum OWD with zero
   standing queue along the path, and sufficiently short so as to timely
   reflect "true" changes in minimum OWD introduced by route changes and
   other rare events.

   The potential drawback in relying on relative OWD as the congestion
   signal is that when multiple flows share the same bottleneck, the
   flow arriving late at the network experiencing a non-empty queue may
   mistakenly consider the standing queuing delay as part of the fixed
   path propagation delay.  This will lead to slightly unfair bandwidth
   sharing among the flows.

   Alternatively, one could move the per-packet statistical handling to
   the sender instead and use relative round-trip-time (RTT) in lieu of
   relative OWD, assuming that per-packet acknowledgements are
   available.  The main drawback of RTT-based approach is the noise in
   the measured delay in the reverse direction.

   Note that the choice of either delay metric (relative OWD vs. RTT)
   involves no change in the proposed rate adaptation algorithm.
   Therefore, comparing the pros and cons regarding which delay metric
   to adopt can 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 accuracy of its delay measurement and estimation
   module.  Appendix A in [RFC6817] provides an extensive discussion on
   this aspect.

   The current recommended practice of simply applying a 15-tab minimum
   filter suffices in guarding against processing delay outliers

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   observed in wired connections.  For 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 parameter values

   In the gradual rate update mode, the parameter TAU indicates the
   upper bound of round-trip-time (RTT) in feedback control loop.
   Typically, the observed feedback interval delta is close to the
   target feedback interval DELTA, and the relative ratio of delta/TAU
   versus ETA dictates the relative strength of influence from the
   aggregate congestion signal offset term (x_offset) versus its recent
   change (x_diff), respectively.  These two terms are analogous to the
   integral and proportional terms in a proportional-integral (PI)
   controller.  The recommended choice of TAU=500ms, DELTA=100ms and ETA
   = 2.0 corresponds to a relative ratio of 1:10 between the gains of
   the integral and proportional terms.  Consequently, the rate
   adaptation is mostly driven by the change in the congestion signal
   with a 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 strike a balance between
   responsiveness and stability.

   The choice of the target feedback interval DELTA needs to strike the
   right balance between timely feedback and low RTCP feedback message
   counts.  A target feedback interval of DELTA=100ms is recommended,
   corresponding to a feedback bandwidth of 16Kbps with 200 bytes per
   feedback message --- approximately 1.6% 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 feedback control loop of NADA congestion control.

   In calculating the non-linear warping of delay in (1), the current
   design uses fixed values of QTH for determining whether to perform
   the non-linear warping).  It is possible to adapt its value based on
   past observed patterns of queuing delay in the presence of packet

   In calculating the aggregate congestion signal x_curr, the choice of
   DMARK and DLOSS influence the steady-state packet loss/marking ratio
   experienced by the 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 impact of individual packet
   loss/marking events.  While the value of DMARK and DLOSS are fixed

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   and predetermined in the current design, a scheme for automatically
   tuning these values based on desired bandwidth sharing behavior in
   the presence of other competing loss-based flows (e.g., loss-based
   TCP) is under investigation.

6.4.  Sender-based vs. receiver-based calculation

   In the current design, the aggregated congestion signal x_curr is
   calculated at the receiver, keeping the sender operation completely
   independent of the form of actual network congestion indications
   (delay, loss, or marking).  Alternatively, one can move the logics of
   (1) and (2) to the sender.  Such an approach requires slightly higher
   overhead in the feedback messages, which should contain individual
   fields on queuing delay (d_queue), packet loss ratio (p_loss), packet
   marking ratio (p_mark), receiving rate (r_recv), and recommended rate
   adaptation mode (rmode).

6.5.  Incremental 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 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 [ns-2] and [ns-3] simulation
   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.ietf-rmcat-eval-test] have been presented at recent IETF
   meetings [IETF-90][IETF-91].  Evaluation results of the current draft
   over several test cases in [I-D.ietf-rmcat-wireless-tests] have been
   presented at [IETF-93].

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   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

8.  Suggested Experiments

   NADA has been extensively evaluated under various test scenarios,
   including the collection of test cases specified by
   [I-D.ietf-rmcat-eval-test] and the subset of WiFi-based test cases in
   [I-D.ietf-rmcat-wireless-tests].  Additional evaluations have been
   carried out to characterize how NADA interacts with various active
   queue management (AQM) schemes such as RED, CoDel, and PIE.  Most of
   these evaluations have been carried out in simulators.  A few key
   test cases have also bee evaluated in implementations embedded in
   video conferencing clients.

   Further experiments are suggested for the following scenarios:

   o  Experiments reflecting the set up of a typical WAN connection.

   o  Experiments with ECN marking capability turned on at the network
      for existing test cases.

   o  Experiments with multiple RMCAT streams bearing different user-
      specified priorities.

   o  Experiments with additional access technologies, especially over
      cellular networks such as 3G/LTE.

   o  Experiments with various media source contents, including audio
      only, audio and video, and application content sharing (e.g.,
      slide shows).

9.  IANA Considerations

   This document makes no request of IANA.

10.  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.  Thanks to Charles Ganzhorn for contributing to the testbed-
   based evaluation of NADA during an early stage of its development.

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11.  References

11.1.  Normative References

              Zanaty, M., Singh, V., Nandakumar, S., and Z. Sarker,
              "Congestion Control and Codec interactions in RTP
              Applications", draft-ietf-rmcat-cc-codec-interactions-02
              (work in progress), March 2016.

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

              Sarker, Z., Singh, V., Zhu, X., and M. Ramalho, "Test
              Cases for Evaluating RMCAT Proposals", draft-ietf-rmcat-
              eval-test-05 (work in progress), April 2017.

              Zhu, X., Cruz, S., and Z. Sarker, "Modeling Video Traffic
              Sources for RMCAT Evaluations", draft-ietf-rmcat-video-
              traffic-model-03 (work in progress), July 2017.

              Sarker, Z., Johansson, I., Zhu, X., Fu, J., Tan, W., and
              M. Ramalho, "Evaluation Test Cases for Interactive Real-
              Time Media over Wireless Networks", draft-ietf-rmcat-
              wireless-tests-04 (work in progress), May 2017.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

   [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,

   [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, <>.

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   [RFC6679]  Westerlund, M., Johansson, I., Perkins, C., O'Hanlon, P.,
              and K. Carlberg, "Explicit Congestion Notification (ECN)
              for RTP over UDP", RFC 6679, DOI 10.17487/RFC6679, August
              2012, <>.

11.2.  Informative References

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

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

   [IETF-90]  Zhu, X., Ramalho, M., Ganzhorn, C., Jones, P., and R. Pan,
              "NADA Update: Algorithm, Implementation, and Test Case
              Evalua6on Results", July 2014,

   [IETF-91]  Zhu, X., Pan, R., Ramalho, M., Mena, S., Ganzhorn, C.,
              Jones, P., and S. D'Aronco, "NADA Algorithm Update and
              Test Case Evaluations", November 2014,

   [IETF-93]  Zhu, X., Pan, R., Ramalho, M., Mena, S., Ganzhorn, C.,
              Jones, P., D'Aronco, S., and J. Fu, "Updates on NADA",
              July 2015, <

   [ns-2]     "The Network Simulator - ns-2",

   [ns-3]     "The Network Simulator - ns-3", <>.

   [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,

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   [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,

   [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,

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012,

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

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 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.  The system can scale easily with a large
   number of video flows and at high link capacity.

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 aggregated congestion signal
   x_curr.  No special action is required at network node.

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

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   [RFC2309].  Calculation of the marking probability involves the
   following steps:

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

           calculate marking probability p as:

              / 0,                    if q < q_lo;
              |        q_avg - q_lo
          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_curr at the receiver.  No changes are required at
   the sender.

A.3.  Random Early Marking with Virtual Queues

   Advanced network nodes may support random early marking based on a
   token bucket algorithm originally designed for Pre-Congestion
   Notification (PCN) [RFC6660].  The early congestion notification
   (ECN) bit in the IP 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 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.

   Calculation of the marking probability involves the following steps:

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

           update token level b_tk;

           calculate the marking probability as:

            / 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
            \ 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 to be below capacity, resulting in
   slight under-utilization of the link.  The maximum marking
   probability is p_max.

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

Authors' Addresses

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


   Rong Pan
   Cisco Systems
   3625 Cisco Way
   San Jose, CA  95134


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   Michael A. Ramalho
   Cisco Systems, Inc.
   8000 Hawkins Road
   Sarasota, FL  34241

   Phone: +1 919 476 2038

   Sergio Mena de la Cruz
   Cisco Systems
   EPFL, Quartier de l'Innovation, Batiment E
   Ecublens, Vaud  1015


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


   Jiantao Fu
   Cisco Systems
   707 Tasman Drive
   Milpitas, CA  95035


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


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