RFC 8699

Internet Engineering Task Force (IETF)                          S. Islam
Request for Comments: 8699                                      M. Welzl
Category: Experimental                                       S. Gjessing
ISSN: 2070-1721                                       University of Oslo
                                                            January 2020

                Coupled Congestion Control for RTP Media


   When multiple congestion-controlled Real-time Transport Protocol
   (RTP) sessions traverse the same network bottleneck, combining their
   controls can improve the total on-the-wire behavior in terms of
   delay, loss, and fairness.  This document describes such a method for
   flows that have the same sender, in a way that is as flexible and
   simple as possible while minimizing the number of changes needed to
   existing RTP applications.  This document also specifies how to apply
   the method for the Network-Assisted Dynamic Adaptation (NADA)
   congestion control algorithm and provides suggestions on how to apply
   it to other congestion control algorithms.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for examination, experimental implementation, and

   This document defines an Experimental Protocol for the Internet
   community.  This document is a product of the Internet Engineering
   Task Force (IETF).  It represents the consensus of the IETF
   community.  It has received public review and has been approved for
   publication by the Internet Engineering Steering Group (IESG).  Not
   all documents approved by the IESG are candidates for any level of
   Internet Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at

Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction
   2.  Definitions
   3.  Limitations
   4.  Architectural Overview
   5.  Roles
     5.1.  SBD
     5.2.  FSE
     5.3.  Flows
       5.3.1.  Example Algorithm 1 - Active FSE
       5.3.2.  Example Algorithm 2 - Conservative Active FSE
   6.  Application
     6.1.  NADA
     6.2.  General Recommendations
   7.  Expected Feedback from Experiments
   8.  IANA Considerations
   9.  Security Considerations
   10. References
     10.1.  Normative References
     10.2.  Informative References
   Appendix A.  Application to GCC
   Appendix B.  Scheduling
   Appendix C.  Example Algorithm - Passive FSE
     C.1.  Example Operation (Passive)

   Authors' Addresses

1.  Introduction

   When there is enough data to send, a congestion controller attempts
   to increase its sending rate until the path's capacity has been
   reached.  Some controllers detect path capacity by increasing the
   sending rate further, until packets are ECN-marked [RFC8087] or
   dropped, and then decreasing the sending rate until that stops
   happening.  This process inevitably creates undesirable queuing delay
   when multiple congestion-controlled connections traverse the same
   network bottleneck, and each connection overshoots the path capacity
   as it determines its sending rate.

   The Congestion Manager (CM) [RFC3124] couples flows by providing a
   single congestion controller.  It is hard to implement because it
   requires an additional congestion controller and removes all per-
   connection congestion control functionality, which is quite a
   significant change to existing RTP-based applications.  This document
   presents a method to combine the behavior of congestion control
   mechanisms that is easier to implement than the Congestion Manager
   [RFC3124] and also requires fewer significant changes to existing
   RTP-based applications.  It attempts to roughly approximate the CM
   behavior by sharing information between existing congestion
   controllers.  It is able to honor user-specified priorities, which is
   required by WebRTC [RTCWEB-OVERVIEW] [RFC7478].

   The described mechanisms are believed safe to use, but they are
   experimental and are presented for wider review and operational

2.  Definitions

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

   Available Bandwidth:
         The available bandwidth is the nominal link capacity minus the
         amount of traffic that traversed the link during a certain time
         interval, divided by that time interval.

         The first link with the smallest available bandwidth along the
         path between a sender and receiver.

         A flow is the entity that congestion control is operating on.
         It could, for example, be a transport-layer connection or an
         RTP stream [RFC7656], regardless of whether or not this RTP
         stream is multiplexed onto an RTP session with other RTP

   Flow Group Identifier (FGI):
         A unique identifier for each subset of flows that is limited by
         a common bottleneck.

   Flow State Exchange (FSE):
         The entity that maintains information that is exchanged between

   Flow Group (FG):
         A group of flows having the same FGI.

   Shared Bottleneck Detection (SBD):
         The entity that determines which flows traverse the same
         bottleneck in the network or the process of doing so.

3.  Limitations

   Sender-side only:
         Shared bottlenecks can exist when multiple flows originate from
         the same sender or when flows from different senders reach the
         same receiver (see Section 3 of [RFC8382]).  Coupled congestion
         control, as described here, only supports the former case, not
         the latter, as it operates inside a single host on the sender

   Shared bottlenecks do not change quickly:
         As per the definition above, a bottleneck depends on cross
         traffic, and since such traffic can heavily fluctuate,
         bottlenecks can change at a high frequency (e.g., there can be
         oscillation between two or more links).  This means that, when
         flows are partially routed along different paths, they may
         quickly change between sharing and not sharing a bottleneck.
         For simplicity, here it is assumed that a shared bottleneck is
         valid for a time interval that is significantly longer than the
         interval at which congestion controllers operate.  Note that,
         for the only SBD mechanism defined in this document
         (multiplexing on the same five-tuple), the notion of a shared
         bottleneck stays correct even in the presence of fast traffic
         fluctuations; since all flows that are assumed to share a
         bottleneck are routed in the same way, if the bottleneck
         changes, it will still be shared.

4.  Architectural Overview

   Figure 1 shows the elements of the architecture for coupled
   congestion control: the Flow State Exchange (FSE), Shared Bottleneck
   Detection (SBD), and Flows.  The FSE is a storage element that can be
   implemented in two ways: active and passive.  In the active version,
   it initiates communication with flows and SBD.  However, in the
   passive version, it does not actively initiate communication with
   flows and SBD; its only active role is internal state maintenance
   (e.g., an implementation could use soft state to remove a flow's data
   after long periods of inactivity).  Every time a flow's congestion
   control mechanism would normally update its sending rate, the flow
   instead updates information in the FSE and performs a query on the
   FSE, leading to a sending rate that can be different from what the
   congestion controller originally determined.  Using information
   about/from the currently active flows, SBD updates the FSE with the
   correct Flow Group Identifiers (FGIs).

   This document describes both active and passive versions.  While the
   passive algorithm works better for congestion controls with RTT-
   independent convergence, it can still produce oscillations on short
   time scales.  The passive algorithm, described in Appendix C, is
   therefore considered highly experimental and not safe to deploy
   outside of testbed environments.  Figure 2 shows the interaction
   between flows and the FSE using the variable names defined in
   Section 5.2.

                         -------  <---  Flow 1
                         | FSE |  <---  Flow 2 ..
                         -------  <---  .. Flow N
                            |             |
                         -------          |
                         | SBD |  <-------|

             Figure 1: Coupled congestion control architecture

     Flow#1(cc)                     FSE                    Flow#2(cc)
     ----------                     ---                    ----------
     #1 JOIN     ----register--> REGISTER

                                 REGISTER    <--register-- JOIN #1

     #2 CC_R(1)  ----UPDATE----> UPDATE (in)

     #3 NEW RATE <---FSE_R(1)-- UPDATE (out) --FSE_R(2)-> #3 NEW RATE

                      Figure 2: Flow-FSE interactions

   Since everything shown in Figure 1 is assumed to operate on a single
   host (the sender) only, this document only describes aspects that
   have an influence on the resulting on-the-wire behavior.  It does
   not, for instance, define how many bits must be used to represent
   FGIs or in which way the entities communicate.

   Implementations can take various forms; for instance, all the
   elements in the figure could be implemented within a single
   application, thereby operating on flows generated by that application
   only.  Another alternative could be to implement both the FSE and SBD
   together in a separate process that different applications
   communicate with via some form of Inter-Process Communication (IPC).
   Such an implementation would extend the scope to flows generated by
   multiple applications.  The FSE and SBD could also be included in the
   Operating System kernel.  However, only one type of coupling
   algorithm should be used for all flows.  Combinations of multiple
   algorithms at different aggregation levels (e.g., the Operating
   System coupling application aggregates with one algorithm, and
   applications coupling their flows with another) have not been tested
   and are therefore not recommended.

5.  Roles

   This section gives an overview of the roles of the elements of
   coupled congestion control and provides an example of how coupled
   congestion control can operate.

5.1.  SBD

   SBD uses knowledge about the flows to determine which flows belong in
   the same Flow Group (FG) and assigns FGIs accordingly.  This
   knowledge can be derived in three basic ways:

   1.  From multiplexing: It can be based on the simple assumption that
       packets sharing the same five-tuple (IP source and destination
       address, protocol, and transport-layer port number pair) and
       having the same values for the Differentiated Services Code Point
       (DSCP) and the ECN field in the IP header are typically treated
       in the same way along the path.  This method is the only one
       specified in this document; SBD MAY consider all flows that use
       the same five-tuple, DSCP, and ECN field value to belong to the
       same FG.  This classification applies to certain tunnels or RTP
       flows that are multiplexed over one transport (cf.
       [TRANSPORT-MULTIPLEX]).  Such multiplexing is also a recommended
       usage of RTP in WebRTC [RTCWEB-RTP-USAGE].

   2.  Via configuration: e.g., by assuming that a common wireless
       uplink is also a shared bottleneck.

   3.  From measurements: e.g., by considering correlations among
       measured delay and loss as an indication of a shared bottleneck.

   The methods above have some essential trade-offs.  For example,
   multiplexing is a completely reliable measure, but it is limited in
   scope to two endpoints (i.e., it cannot be applied to couple
   congestion controllers of one sender talking to multiple receivers).
   A measurement-based SBD mechanism is described in [RFC8382].
   Measurements can never be 100% reliable, in particular because they
   are based on the past, but applying coupled congestion control
   involves making an assumption about the future; it is therefore
   recommended to implement cautionary measures, e.g., by disabling
   coupled congestion control if enabling it causes a significant
   increase in delay and/or packet loss.  Measurements also take time,
   which entails a certain delay for turning on coupling (refer to
   [RFC8382] for details).  When this is possible, it can be more
   efficient to statically configure shared bottlenecks (e.g., via a
   system configuration or user input) based on assumptions about the
   network environment.

5.2.  FSE

   The FSE contains a list of all flows that have registered with it.
   For each flow, the FSE stores the following:

   *  a unique flow number f to identify the flow.

   *  the FGI of the FG that it belongs to (based on the definitions in
      this document, a flow has only one bottleneck and can therefore be
      in only one FG).

   *  a priority P(f), which is a number greater than zero.

   *  The rate used by the flow in bits per second, FSE_R(f).

   *  The desired rate DR(f) of flow f.  This can be smaller than
      FSE_R(f) if the application feeding into the flow has less data to
      send than FSE_R(f) would allow or if a maximum value is imposed on
      the rate.  In the absence of such limits, DR(f) must be set to the
      sending rate provided by the congestion control module of flow f.

   Note that the absolute range of priorities does not matter; the
   algorithm works with a flow's priority portion of the sum of all
   priority values.  For example, if there are two flows, flow 1 with
   priority 1 and flow 2 with priority 2, the sum of the priorities is
   3.  Then, flow 1 will be assigned 1/3 of the aggregate sending rate,
   and flow 2 will be assigned 2/3 of the aggregate sending rate.
   Priorities can be mapped to the "very-low", "low", "medium", or
   "high" priority levels described in [WEBRTC-TRANS] by simply using
   the values 1, 2, 4, and 8, respectively.

   In the FSE, each FG contains one static variable, S_CR, which is the
   sum of the calculated rates of all flows in the same FG.  This value
   is used to calculate the sending rate.

   The information listed here is enough to implement the sample flow
   algorithm given below.  FSE implementations could easily be extended
   to store, e.g., a flow's current sending rate for statistics
   gathering or future potential optimizations.

5.3.  Flows

   Flows register themselves with SBD and FSE when they start,
   deregister from the FSE when they stop, and carry out an UPDATE
   function call every time their congestion controller calculates a new
   sending rate.  Via UPDATE, they provide the newly calculated rate
   and, optionally (if the algorithm supports it), the desired rate.
   The desired rate is less than the calculated rate in case of
   application-limited flows; otherwise, it is the same as the
   calculated rate.

   Below, two example algorithms are described.  While other algorithms
   could be used instead, the same algorithm must be applied to all
   flows.  Names of variables used in the algorithms are explained

   CC_R(f)   The rate received from the congestion controller of flow f
             when it calls UPDATE.

   FSE_R(f)  The rate calculated by the FSE for flow f.

   DR(f)     The desired rate of flow f.

   S_CR      The sum of the calculated rates of all flows in the same
             FG; this value is used to calculate the sending rate.

   FG        A group of flows having the same FGI and hence, sharing the
             same bottleneck.

   P(f)      The priority of flow f, which is received from the flow's
             congestion controller; the FSE uses this variable for
             calculating FSE_R(f).

   S_P       The sum of all the priorities.

   TLO       The total leftover rate; the sum of rates that could not be
             assigned to flows that were limited by their desired rate.

   AR        The aggregate rate that is assigned to flows that are not
             limited by their desired rate.

5.3.1.  Example Algorithm 1 - Active FSE

   This algorithm was designed to be the simplest possible method to
   assign rates according to the priorities of flows.  Simulation
   results in [FSE] indicate that it does not, however, significantly
   reduce queuing delay and packet loss.

   (1)  When a flow f starts, it registers itself with SBD and the FSE.
        FSE_R(f) is initialized with the congestion controller's initial
        rate.  SBD will assign the correct FGI.  When a flow is assigned
        an FGI, it adds its FSE_R(f) to S_CR.

   (2)  When a flow f stops or pauses, its entry is removed from the

   (3)  Every time the congestion controller of the flow f determines a
        new sending rate CC_R(f), the flow calls UPDATE, which carries
        out the tasks listed below to derive the new sending rates for
        all the flows in the FG.  A flow's UPDATE function uses three
        local (i.e., per-flow) temporary variables: S_P, TLO, and AR.

        (a)  It updates S_CR.

                       S_CR = S_CR + CC_R(f) - FSE_R(f)

        (b)  It calculates the sum of all the priorities, S_P, and
             initializes FSE_R.

                       S_P = 0
                       for all flows i in FG do
                           S_P = S_P + P(i)
                           FSE_R(i) = 0
                       end for

        (c)  It distributes S_CR among all flows, ensuring that each
             flow's desired rate is not exceeded.

                       TLO = S_CR
                       while(TLO-AR>0 and S_P>0)
                           AR = 0
                           for all flows i in FG do
                               if FSE_R[i] < DR[i] then
                                   if TLO * P[i] / S_P >= DR[i] then
                                       TLO = TLO - DR[i]
                                       FSE_R[i] = DR[i]
                                       S_P = S_P - P[i]
                                       FSE_R[i] = TLO * P[i] / S_P
                                       AR = AR + TLO * P[i] / S_P
                                   end if
                               end if
                           end for
                       end while

        (d)  It distributes FSE_R to all the flows.

                       for all flows i in FG do
                           send FSE_R(i) to the flow i
                       end for

5.3.2.  Example Algorithm 2 - Conservative Active FSE

   This algorithm changes algorithm 1 to conservatively emulate the
   behavior of a single flow by proportionally reducing the aggregate
   rate on congestion.  Simulation results in [FSE] indicate that it can
   significantly reduce queuing delay and packet loss.

   Step (a) of the UPDATE function is changed as described below.  This
   also introduces a local variable DELTA, which is used to calculate
   the difference between CC_R(f) and the previously stored FSE_R(f).
   To prevent flows from either ignoring congestion or overreacting, a
   timer keeps them from changing their rates immediately after the
   common rate reduction that follows a congestion event.  This timer is
   set to two RTTs of the flow that experienced congestion because it is
   assumed that a congestion event can persist for up to one RTT of that
   flow, with another RTT added to compensate for fluctuations in the
   measured RTT value.

   (a)  It updates S_CR based on DELTA.

                  if Timer has expired or was not set then
                    DELTA = CC_R(f) - FSE_R(f)
                    if DELTA < 0 then  // Reduce S_CR proportionally
                      S_CR = S_CR * CC_R(f) / FSE_R(f)
                      Set Timer for 2 RTTs
                      S_CR = S_CR + DELTA
                    end if
                   end if

6.  Application

   This section specifies how the FSE can be applied to specific
   congestion control mechanisms and makes general recommendations that
   facilitate applying the FSE to future congestion controls.

6.1.  NADA

   Network-Assisted Dynamic Adaptation (NADA) [RFC8698] is a congestion
   control scheme for WebRTC.  It calculates a reference rate r_ref upon
   receiving an acknowledgment and then, based on the reference rate,
   calculates a video target rate r_vin and a sending rate for the
   flows, r_send.

   When applying the FSE to NADA, the UPDATE function call described in
   Section 5.3 gives the FSE NADA's reference rate r_ref.  The
   recommended algorithm for NADA is the Active FSE in Section 5.3.1.
   In step 3 (d), when the FSE_R(i) is "sent" to the flow i, r_ref
   (r_vin and r_send) of flow i is updated with the value of FSE_R(i).

6.2.  General Recommendations

   This section provides general advice for applying the FSE to
   congestion control mechanisms.

   Receiver-side calculations:
         When receiver-side calculations make assumptions about the rate
         of the sender, the calculations need to be synchronized, or the
         receiver needs to be updated accordingly.  This applies to TCP
         Friendly Rate Control (TFRC) [RFC5348], for example, where
         simulations showed somewhat less favorable results when using
         the FSE without a receiver-side change [FSE].

   Stateful algorithms:
         When a congestion control algorithm is stateful (e.g., during
         the TCP slow start, congestion avoidance, or fast recovery
         phase), these states should be carefully considered such that
         the overall state of the aggregate flow is correct.  This may
         require sharing more information in the UPDATE call.

   Rate jumps:
         The FSE-based coupling algorithms can let a flow quickly
         increase its rate to its fair share, e.g., when a new flow
         joins or after a quiescent period.  In case of window-based
         congestion controls, this may produce a burst that should be
         mitigated in some way.  An example of how this could be done
         without using a timer is presented in [ANRW2016], using TCP as
         an example.

7.  Expected Feedback from Experiments

   The algorithm described in this memo has so far been evaluated using
   simulations covering all the tests for more than one flow from
   [RMCAT-PROPOSALS] (see [IETF-93] and [IETF-94]).  Experiments should
   confirm these results using at least the NADA congestion control
   algorithm with real-life code (e.g., browsers communicating over an
   emulated network covering the conditions in [RMCAT-PROPOSALS]).  The
   tests with real-life code should be repeated afterwards in real
   network environments and monitored.  Experiments should investigate
   cases where the media coder's output rate is below the rate that is
   calculated by the coupling algorithm (FSE_R(i) in algorithms 1
   (Section 5.3.1) and 2 (Section 5.3.2)).  Implementers and testers are
   invited to document their findings in an Internet-Draft.

8.  IANA Considerations

   This document has no IANA actions.

9.  Security Considerations

   In scenarios where the architecture described in this document is
   applied across applications, various cheating possibilities arise,
   e.g., supporting wrong values for the calculated rate, desired rate,
   or priority of a flow.  In the worst case, such cheating could either
   prevent other flows from sending or make them send at a rate that is
   unreasonably large.  The end result would be unfair behavior at the
   network bottleneck, akin to what could be achieved with any UDP-based
   application.  Hence, since this is no worse than UDP in general,
   there seems to be no significant harm in using this in the absence of
   UDP rate limiters.

   In the case of a single-user system, it should also be in the
   interest of any application programmer to give the user the best
   possible experience by using reasonable flow priorities or even
   letting the user choose them.  In a multi-user system, this interest
   may not be given, and one could imagine the worst case of an "arms
   race" situation where applications end up setting their priorities to
   the maximum value.  If all applications do this, the end result is a
   fair allocation in which the priority mechanism is implicitly
   eliminated and no major harm is done.

   Implementers should also be aware of the Security Considerations
   sections of [RFC3124], [RFC5348], and [RFC7478].

10.  References

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,

   [RFC3124]  Balakrishnan, H. and S. Seshan, "The Congestion Manager",
              RFC 3124, DOI 10.17487/RFC3124, June 2001,

   [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,

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

   [RFC8698]  Zhu, X., Pan, R., Ramalho, M., and S. Mena, "Network-
              Assisted Dynamic Adaptation (NADA): A Unified Congestion
              Control Scheme for Real-Time Media", RFC 8698,
              DOI 10.17487/RFC8698, January 2020,

10.2.  Informative References

   [ANRW2016] Islam, S. and M. Welzl, "Start Me Up: Determining and
              Sharing TCP's Initial Congestion Window", ACM, IRTF, ISOC
              Applied Networking Research Workshop 2016 (ANRW 2016),
              DOI 10.1145/2959424.2959440, Proceedings of the 2016
              Applied Networking Research Workshop Pages 52-54, July
              2016, <https://doi.org/10.1145/2959424.2959440>.

   [FSE]      Islam, S., Welzl, M., Gjessing, S., and N. Khademi,
              "Coupled Congestion Control for RTP Media", ACM SIGCOMM
              Capacity Sharing Workshop (CSWS 2014) and ACM SIGCOMM CCR
              44(4) 2014, March 2014,

   [FSE-NOMS] Islam, S., Welzl, M., Hayes, D., and S. Gjessing,
              "Managing real-time media flows through a flow state
              exchange", IEEE NOMS 2016, DOI 10.1109/NOMS.2016.7502803,

              Holmer, S., Lundin, H., Carlucci, G., Cicco, L., and S.
              Mascolo, "A Google Congestion Control Algorithm for Real-
              Time Communication", Work in Progress, Internet-Draft,
              draft-ietf-rmcat-gcc-02, 8 July 2016,

   [IETF-93]  Islam, S., Welzl, M., and S. Gjessing, "Updates on
              'Coupled Congestion Control for RTP Media'", RTP Media
              Congestion Avoidance Techniques (rmcat) Working Group,
              IETF 93, July 2015,

   [IETF-94]  Islam, S., Welzl, M., and S. Gjessing, "Updates on
              'Coupled Congestion Control for RTP Media'", RTP Media
              Congestion Avoidance Techniques (rmcat) Working Group,
              IETF 94, November 2015,

   [RFC7478]  Holmberg, C., Hakansson, S., and G. Eriksson, "Web Real-
              Time Communication Use Cases and Requirements", RFC 7478,
              DOI 10.17487/RFC7478, March 2015,

   [RFC7656]  Lennox, J., Gross, K., Nandakumar, S., Salgueiro, G., and
              B. Burman, Ed., "A Taxonomy of Semantics and Mechanisms
              for Real-Time Transport Protocol (RTP) Sources", RFC 7656,
              DOI 10.17487/RFC7656, November 2015,

   [RFC8087]  Fairhurst, G. and M. Welzl, "The Benefits of Using
              Explicit Congestion Notification (ECN)", RFC 8087,
              DOI 10.17487/RFC8087, March 2017,

   [RFC8382]  Hayes, D., Ed., Ferlin, S., Welzl, M., and K. Hiorth,
              "Shared Bottleneck Detection for Coupled Congestion
              Control for RTP Media", RFC 8382, DOI 10.17487/RFC8382,
              June 2018, <https://www.rfc-editor.org/info/rfc8382>.

              Sarker, Z., Singh, V., Zhu, X., and M. Ramalho, "Test
              Cases for Evaluating RMCAT Proposals", Work in Progress,
              Internet-Draft, draft-ietf-rmcat-eval-test-10, 23 May
              2019, <https://tools.ietf.org/html/draft-ietf-rmcat-eval-

              Alvestrand, H., "Overview: Real Time Protocols for
              Browser-based Applications", Work in Progress, Internet-
              Draft, draft-ietf-rtcweb-overview-19, 11 November 2017,

              Perkins, C., Westerlund, M., and J. Ott, "Web Real-Time
              Communication (WebRTC): Media Transport and Use of RTP",
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Appendix A.  Application to GCC

   Google Congestion Control (GCC) [GCC-RTCWEB] is another congestion
   control scheme for RTP flows that is under development.  GCC is not
   yet finalized, but at the time of this writing, the rate control of
   GCC employs two parts: controlling the bandwidth estimate based on
   delay and controlling the bandwidth estimate based on loss.  Both are
   designed to estimate the available bandwidth, A_hat.

   When applying the FSE to GCC, the UPDATE function call described in
   Section 5.3 gives the FSE GCC's estimate of available bandwidth
   A_hat.  The recommended algorithm for GCC is the Active FSE in
   Section 5.3.1.  In step 3 (d) of this algorithm, when the FSE_R(i) is
   "sent" to the flow i, A_hat of flow i is updated with the value of

Appendix B.  Scheduling

   When flows originate from the same host, it would be possible to use
   only one sender-side congestion controller that determines the
   overall allowed sending rate and then use a local scheduler to assign
   a proportion of this rate to each RTP session.  This way, priorities
   could also be implemented as a function of the scheduler.  The
   Congestion Manager (CM) [RFC3124] also uses such a scheduling

Appendix C.  Example Algorithm - Passive FSE

   Active algorithms calculate the rates for all the flows in the FG and
   actively distribute them.  In a passive algorithm, UPDATE returns a
   rate that should be used instead of the rate that the congestion
   controller has determined.  This can make a passive algorithm easier
   to implement; however, when round-trip times of flows are unequal,
   flows with shorter RTTs may (depending on the congestion control
   algorithm) update and react to the overall FSE state more often than
   flows with longer RTTs, which can produce unwanted side effects.
   This problem is more significant when the congestion control
   convergence depends on the RTT.  While the passive algorithm works
   better for congestion controls with RTT-independent convergence, it
   can still produce oscillations on short time scales.  The algorithm
   described below is therefore considered highly experimental and not
   safe to deploy outside of testbed environments.  Results of a
   simplified passive FSE algorithm with both NADA and GCC can be found
   in [FSE-NOMS].

   In the passive version of the FSE, TLO (Total Leftover Rate) is a
   static variable per FG that is initialized to 0.  Additionally, S_CR
   is limited to increase or decrease as conservatively as a flow's
   congestion controller decides in order to prohibit sudden rate jumps.

   (1)  When a flow f starts, it registers itself with SBD and the FSE.
        FSE_R(f) and DR(f) are initialized with the congestion
        controller's initial rate.  SBD will assign the correct FGI.
        When a flow is assigned an FGI, it adds its FSE_R(f) to S_CR.

   (2)  When a flow f stops or pauses, it sets its DR(f) to 0 and sets
        P(f) to -1.

   (3)  Every time the congestion controller of the flow f determines a
        new sending rate CC_R(f), assuming the flow's new desired rate
        new_DR(f) to be "infinity" in case of a bulk data transfer with
        an unknown maximum rate, the flow calls UPDATE, which carries
        out the tasks listed below to derive the flow's new sending
        rate, Rate(f).  A flow's UPDATE function uses a few local (i.e.,
        per-flow) temporary variables, which are all initialized to 0:
        DELTA, new_S_CR, and S_P.

        (a)  For all the flows in its FG (including itself), it
             calculates the sum of all the calculated rates, new_S_CR.
             Then, it calculates DELTA: the difference between FSE_R(f)
             and CC_R(f).

                     for all flows i in FG do
                         new_S_CR = new_S_CR + FSE_R(i)
                     end for
                     DELTA =  CC_R(f) - FSE_R(f)

        (b)  It updates S_CR, FSE_R(f), and DR(f).

                     FSE_R(f) = CC_R(f)
                     if DELTA > 0 then  // the flow's rate has increased
                         S_CR = S_CR + DELTA
                     else if DELTA < 0 then
                         S_CR = new_S_CR + DELTA
                     end if
                     DR(f) = min(new_DR(f),FSE_R(f))

        (c)  It calculates the leftover rate TLO, removes the terminated
             flows from the FSE, and calculates the sum of all the
             priorities, S_P.

                       for all flows i in FG do
                          if P(i)<0 then
                             delete flow
                             S_P = S_P + P(i)
                          end if
                       end for
                       if DR(f) < FSE_R(f) then
                          TLO = TLO + (P(f)/S_P) * S_CR - DR(f))
                       end if

        (d)  It calculates the sending rate, Rate(f).

                       Rate(f) = min(new_DR(f), (P(f)*S_CR)/S_P + TLO)

                       if Rate(f) != new_DR(f) and TLO > 0 then
                           TLO = 0  // f has 'taken' TLO
                       end if

        (e)  It updates DR(f) and FSE_R(f) with Rate(f).

                       if Rate(f) > DR(f) then
                           DR(f) = Rate(f)
                       end if
                       FSE_R(f)  = Rate(f)

   The goals of the flow algorithm are to achieve prioritization,
   improve network utilization in the face of application-limited flows,
   and impose limits on the increase behavior such that the negative
   impact of multiple flows trying to increase their rate together is
   minimized.  It does that by assigning a flow a sending rate that may
   not be what the flow's congestion controller expected.  It therefore
   builds on the assumption that no significant inefficiencies arise
   from temporary application-limited behavior or from quickly jumping
   to a rate that is higher than the congestion controller intended.
   How problematic these issues really are depends on the controllers in
   use and requires careful per-controller experimentation.  The coupled
   congestion control mechanism described here also does not require all
   controllers to be equal; effects of heterogeneous controllers, or
   homogeneous controllers being in different states, are also subject
   to experimentation.

   This algorithm gives the leftover rate of application-limited flows
   to the first flow that updates its sending rate, provided that this
   flow needs it all (otherwise, its own leftover rate can be taken by
   the next flow that updates its rate).  Other policies could be
   applied, e.g., to divide the leftover rate of a flow equally among
   all other flows in the FGI.

C.1.  Example Operation (Passive)

   In order to illustrate the operation of the passive coupled
   congestion control algorithm, this section presents a toy example of
   two flows that use it.  Let us assume that both flows traverse a
   common 10 Mbit/s bottleneck and use a simplistic congestion
   controller that starts out with 1 Mbit/s, increases its rate by 1
   Mbit/s in the absence of congestion, and decreases it by 2 Mbit/s in
   the presence of congestion.  For simplicity, flows are assumed to
   always operate in a round-robin fashion.  Rate numbers below without
   units are assumed to be in Mbit/s.  For illustration purposes, the
   actual sending rate is also shown for every flow in FSE diagrams even
   though it is not really stored in the FSE.

   Flow #1 begins.  It is a bulk data transfer and considers itself to
   have top priority.  This is the FSE after the flow algorithm's step

   | # | FGI |  P  | FSE_R  |  DR  | Rate |
   |   |     |     |        |      |      |
   | 1 |  1  |  1  |   1    |   1  |   1  |
   S_CR = 1, TLO = 0

   Its congestion controller gradually increases its rate.  Eventually,
   at some point, the FSE should look like this:

   | # | FGI |  P  |  FSE_R  |  DR  | Rate |
   |   |     |     |         |      |      |
   | 1 |  1  |  1  |   10    |  10  |  10  |
   S_CR = 10, TLO = 0

   Now, another flow joins.  It is also a bulk data transfer and has a
   lower priority (0.5):

   | # | FGI |   P   | FSE_R  |  DR  | Rate |
   |   |     |       |        |      |      |
   | 1 |  1  |   1   |   10   |  10  |  10  |
   | 2 |  1  |  0.5  |    1   |   1  |   1  |
   S_CR = 11, TLO = 0

   Now, assume that the first flow updates its rate to 8, because the
   total sending rate of 11 exceeds the total capacity.  Let us take a
   closer look at what happens in step 3 of the flow algorithm.

   CC_R(1) = 8. new_DR(1) = infinity.

   (3a)  new_S_CR = 11; DELTA = 8 - 10 = -2.

   (3b)  FSE_R(1) = 8.  DELTA is negative, hence S_CR = 9; DR(1) = 8

   (3c)  S_P = 1.5.

   (3d)  new sending rate Rate(1) = min(infinity, 1/1.5 * 9 + 0) = 6.

   (3e)  FSE_R(1) = 6.

   The resulting FSE looks as follows:

   | # | FGI |   P   |  FSE_R  |  DR  | Rate |
   |   |     |       |         |      |      |
   | 1 |  1  |   1   |    6    |   8  |   6  |
   | 2 |  1  |  0.5  |    1    |   1  |   1  |
   S_CR = 9, TLO = 0

   The effect is that flow #1 is sending with 6 Mbit/s instead of the 8
   Mbit/s that the congestion controller derived.  Let us now assume
   that flow #2 updates its rate.  Its congestion controller detects
   that the network is not fully saturated (the actual total sending
   rate is 6+1=7) and increases its rate.

   CC_R(2) = 2. new_DR(2) = infinity.

   (3a)  new_S_CR = 7; DELTA = 2 - 1 = 1.

   (3b)  FSE_R(2) = 2.  DELTA is positive, hence S_CR = 9 + 1 = 10;
         DR(2) = 2.

   (3c)  S_P = 1.5.

   (3d)  Rate(2) = min(infinity, 0.5/1.5 * 10 + 0) = 3.33.

   (3e)  DR(2) = FSE_R(2) = 3.33.

   The resulting FSE looks as follows:

   | # | FGI |   P   |  FSE_R  |  DR  | Rate |
   |   |     |       |         |      |      |
   | 1 |  1  |   1   |    6    |   8  |   6  |
   | 2 |  1  |  0.5  |   3.33  | 3.33 | 3.33 |
   S_CR = 10, TLO = 0

   The effect is that flow #2 is now sending with 3.33 Mbit/s, which is
   close to half of the rate of flow #1 and leads to a total utilization
   of 6(#1) + 3.33(#2) = 9.33 Mbit/s.  Flow #2's congestion controller
   has increased its rate faster than the controller actually expected.
   Now, flow #1 updates its rate.  Its congestion controller detects
   that the network is not fully saturated and increases its rate.
   Additionally, the application feeding into flow #1 limits the flow's
   sending rate to at most 2 Mbit/s.

   CC_R(1) = 7. new_DR(1) = 2.

   (3a)  new_S_CR = 9.33; DELTA = 1.

   (3b)  FSE_R(1) = 7, DELTA is positive, hence S_CR = 10 + 1 = 11;
         DR(1) = min(2, 7) = 2.

   (3c)  S_P = 1.5; DR(1) < FSE_R(1), hence TLO = 1/1.5 * 11 - 2 = 5.33.

   (3d)  Rate(1) = min(2, 1/1.5 * 11 + 5.33) = 2.

   (3e)  FSE_R(1) = 2.

   The resulting FSE looks as follows:

   | # | FGI |   P   |  FSE_R  |  DR  | Rate |
   |   |     |       |         |      |      |
   | 1 |  1  |   1   |    2    |   2  |   2  |
   | 2 |  1  |  0.5  |   3.33  | 3.33 | 3.33 |
   S_CR = 11, TLO = 5.33

   Now, the total rate of the two flows is 2 + 3.33 = 5.33 Mbit/s, i.e.,
   the network is significantly underutilized due to the limitation of
   flow #1.  Flow #2 updates its rate.  Its congestion controller
   detects that the network is not fully saturated and increases its

   CC_R(2) = 4.33. new_DR(2) = infinity.

   (3a)  new_S_CR = 5.33; DELTA = 1.

   (3b)  FSE_R(2) = 4.33.  DELTA is positive, hence S_CR = 12; DR(2) =

   (3c)  S_P = 1.5.

   (3d)  Rate(2) = min(infinity, 0.5/1.5 * 12 + 5.33 ) = 9.33.

   (3e)  FSE_R(2) = 9.33, DR(2) = 9.33.

   The resulting FSE looks as follows:

   | # | FGI |   P   |  FSE_R  |  DR  | Rate |
   |   |     |       |         |      |      |
   | 1 |  1  |   1   |    2    |   2  |   2  |
   | 2 |  1  |  0.5  |   9.33  | 9.33 | 9.33 |
   S_CR = 12, TLO = 0

   Now, the total rate of the two flows is 2 + 9.33 = 11.33 Mbit/s.
   Finally, flow #1 terminates.  It sets P(1) to -1 and DR(1) to 0.  Let
   us assume that it terminated late enough for flow #2 to still
   experience the network in a congested state, i.e., flow #2 decreases
   its rate in the next iteration.

   CC_R(2) = 7.33. new_DR(2) = infinity.

   (3a)  new_S_CR = 11.33; DELTA = -2.

   (3b)  FSE_R(2) = 7.33.  DELTA is negative, hence S_CR = 9.33; DR(2) =

   (3c)  Flow 1 has P(1) = -1, hence it is deleted from the FSE.  S_P =

   (3d)  Rate(2) = min(infinity, 0.5/0.5*9.33 + 0) = 9.33.

   (3e)  FSE_R(2) = DR(2) = 9.33.

   The resulting FSE looks as follows:

   | # | FGI |   P   |  FSE_R  |  DR  | Rate |
   |   |     |       |         |      |      |
   | 2 |  1  |  0.5  |   9.33  | 9.33 | 9.33 |
   S_CR = 9.33, TLO = 0


   This document benefited from discussions with and feedback from
   Andreas Petlund, Anna Brunstrom, Colin Perkins, David Hayes, David
   Ros (who also gave the FSE its name), Ingemar Johansson, Karen
   Nielsen, Kristian Hiorth, Martin Stiemerling, Mirja K├╝hlewind,
   Spencer Dawkins, Varun Singh, Xiaoqing Zhu, and Zaheduzzaman Sarker.
   The authors would like to especially thank Xiaoqing Zhu and Stefan
   Holmer for helping with NADA and GCC, and Anna Brunstrom as well as
   Julius Flohr for helping us correct the active algorithm for the case
   of application-limited flows.

   This work was partially funded by the European Community under its
   Seventh Framework Program through the Reducing Internet Transport
   Latency (RITE) project (ICT-317700).

Authors' Addresses

   Safiqul Islam
   University of Oslo
   PO Box 1080 Blindern
   N-0316 Oslo

   Phone: +47 22 84 08 37
   Email: safiquli@ifi.uio.no

   Michael Welzl
   University of Oslo
   PO Box 1080 Blindern
   N-0316 Oslo

   Phone: +47 22 85 24 20
   Email: michawe@ifi.uio.no

   Stein Gjessing
   University of Oslo
   PO Box 1080 Blindern
   N-0316 Oslo

   Phone: +47 22 85 24 44
   Email: steing@ifi.uio.no