Internet DRAFT - draft-hayes-rmcat-sbd
draft-hayes-rmcat-sbd
RTP Media Congestion Avoidance D. Hayes, Ed.
Techniques University of Oslo
Internet-Draft S. Ferlin
Intended status: Experimental Simula Research Laboratory
Expires: September 4, 2015 M. Welzl
University of Oslo
March 3, 2015
Shared Bottleneck Detection for Coupled Congestion Control for RTP
Media.
draft-hayes-rmcat-sbd-02
Abstract
This document describes a mechanism to detect whether end-to-end data
flows share a common bottleneck. It relies on summary statistics
that are calculated by a data receiver based on continuous
measurements and regularly fed to a grouping algorithm that runs
wherever the knowledge is needed. This mechanism complements the
coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.
Status of this Memo
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This Internet-Draft will expire on September 4, 2015.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3
1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Parameter Values . . . . . . . . . . . . . . . . . . . . . 5
3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Key metrics and their calculation . . . . . . . . . . . . 7
3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 7
3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 8
3.1.3. Variance Estimate . . . . . . . . . . . . . . . . . . 9
3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 9
3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 10
3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 10
3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 10
3.2.2. Using the flow group signal . . . . . . . . . . . . . 12
3.3. Removing Noise from the Estimates . . . . . . . . . . . . 12
3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 12
3.3.2. Clock drift . . . . . . . . . . . . . . . . . . . . . 13
3.3.3. Bias in the skewness measure . . . . . . . . . . . . . 14
3.4. Reducing lag and Improving Responsiveness . . . . . . . . 14
3.4.1. Improving the response of the skewness estimate . . . 15
3.4.2. Improving the response of the variance estimate . . . 15
4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 16
4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 16
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 16
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16
7. Security Considerations . . . . . . . . . . . . . . . . . . . 16
8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 17
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 17
9.1. Normative References . . . . . . . . . . . . . . . . . . . 17
9.2. Informative References . . . . . . . . . . . . . . . . . . 17
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 18
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1. Introduction
In the Internet, it is not normally known if flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths
and share a bottleneck or not. Flows that share a bottleneck link
usually compete with one another for their share of the capacity.
This competition has the potential to increase packet loss and
delays. This is especially relevant for interactive applications
that communicate simultaneously with multiple peers (such as multi-
party video). For RTP media applications such as RTCWEB,
[I-D.welzl-rmcat-coupled-cc] describes a scheme that combines the
congestion controllers of flows in order to honor their priorities
and avoid unnecessary packet loss as well as delay. This mechanism
relies on some form of Shared Bottleneck Detection (SBD); here, a
measurement-based SBD approach is described.
1.1. The signals
The current Internet is unable to explicitly inform endpoints as to
which flows share bottlenecks, so endpoints need to infer this from
whatever information is available to them. The mechanism described
here currently utilises packet loss and packet delay, but is not
restricted to these.
1.1.1. Packet Loss
Packet loss is often a relatively rare signal. Therefore, on its own
it is of limited use for SBD, however, it is a valuable supplementary
measure when it is more prevalent.
1.1.2. Packet Delay
End-to-end delay measurements include noise from every device along
the path in addition to the delay perturbation at the bottleneck
device. The noise is often significantly increased if the round-trip
time is used. The cleanest signal is obtained by using One-Way-Delay
(OWD).
Measuring absolute OWD is difficult since it requires both the sender
and receiver clocks to be synchronised. However, since the
statistics being collected are relative to the mean OWD, a relative
OWD measurement is sufficient. Clock drift is not usually
significant over the time intervals used by this SBD mechanism (see
[RFC6817] A.2 for a discussion on clock drift and OWD measurements).
However, in circumstances where it is significant, Section 3.3.2
outlines a way of adjusting the calculations to cater for it.
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Each packet arriving at the bottleneck buffer may experience very
different queue lengths, and therefore different waiting times. A
single OWD sample does not, therefore, characterize the path well.
However, multiple OWD measurements do reflect the distribution of
delays experienced at the bottleneck.
1.1.3. Path Lag
Flows that share a common bottleneck may traverse different paths,
and these paths will often have different base delays. This makes it
difficult to correlate changes in delay or loss. This technique uses
the long term shape of the delay distribution as a base for
comparison to counter this.
2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
Acronyms used in this document:
OWD -- One Way Delay
PDV -- Packet Delay Variation
RTT -- Round Trip Time
SBD -- Shared Bottleneck Detection
Conventions used in this document:
T -- the base time interval over which measurements are
made.
N -- the number of base time, T, intervals used in some
calculations.
sum_T(...) -- summation of all the measurements of the variable
in parentheses taken over the interval T
sum(...) -- summation of terms of the variable in parentheses
sum_N(...) -- summation of N terms of the variable in parentheses
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sum_NT(...) -- summation of all measurements taken over the
interval N*T
E_T(...) -- the expectation or mean of the measurements of the
variable in parentheses over T
E_N(...) -- The expectation or mean of the last N values of the
variable in parentheses
E_M(...) -- The expectation or mean of the last M values of the
variable in parentheses, where M <= N.
max_T(...) -- the maximum recorded measurement of the variable in
parentheses taken over the interval T
min_T(...) -- the minimum recorded measurement of the variable in
parentheses taken over the interval T
num_T(...) -- the count of measurements of the variable in
parentheses taken in the interval T
num_VM(...) -- the count of valid values of the variable in
parentheses given M records
PC -- a boolean variable indicating the particular flow was
identified as experiencing congestion in the previous
interval T (i.e. Previously Congested)
CD_T -- an estimate of the effect of Clock Drift on the mean
OWD per T
CD_Adj(...) -- Mean OWD adjusted for clock drift
p_l, p_f, p_pdv, c_s, c_h, p_s, p_d, p_v -- various thresholds
used in the mechanism.
N, M, and F -- number of values (calculated over T).
2.1. Parameter Values
Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1. The other
parameters have been tightened to reflect minor enhancements to the
algorithm outlined in Section 3.3: c_s = -0.01, p_f = p_s = p_d =
0.1, p_pdv = 0.2, p_v = 0.2. M=50, F=10, and c_h = 0.3 are
additional parameters defined in the document. These are values that
seem to work well over a wide range of practical Internet conditions,
but are the subject of ongoing tests.
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3. Mechanism
The mechanism described in this document is based on the observation
that the distribution of delay measurements of packets from flows
that share a common bottleneck have similar shape characteristics.
These shape characteristics are described using 3 key summary
statistics:
variance (estimate var_est, see Section 3.1.3)
skewness (estimate skew_est, see Section 3.1.2)
oscillation (estimate freq_est, see Section 3.1.4)
with packet loss (estimate pkt_loss, see Section 3.1.5) used as a
supplementary statistic.
Summary statistics help to address both the noise and the path lag
problems by describing the general shape over a relatively long
period of time. This is sufficient for their application in coupled
congestion control for RTP Media. They can be signalled from a
receiver, which measures the OWD and calculates the summary
statistics, to a sender, which is the entity that is transmitting the
media stream. An RTP Media device may be both a sender and a
receiver. SBD can be performed at either Sender or receiver or both.
+----+
| H2 |
+----+
|
| L2
|
+----+ L1 | L3 +----+
| H1 |------|------| H3 |
+----+ +----+
A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).
Figure 1
In Figure 1, there are two possible cases for shared bottleneck
detection: a sender-based and a receiver-based case.
1. Sender-based: consider a situation where host H1 sends media
streams to hosts H2 and H3, and L1 is a shared bottleneck. H2
and H3 measure the OWD and calculate summary statistics, which
they send to H1 every T. H1, having this knowledge, can determine
the shared bottleneck and accordingly control the send rates.
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2. Receiver-based: consider that H2 is also sending media to H3, and
L3 is a shared bottleneck. If H3 sends summary statistics to H1
and H2, neither H1 nor H2 alone obtain enough knowledge to detect
this shared bottleneck; H3 can however determine it by combining
the summary statistics related to H1 and H2, respectively. This
case is applicable when send rates are controlled by the
receiver; then, the signal from H3 to the senders contains the
sending rate.
A discussion of the required signalling for the receiver-based case
is beyond the scope of this document. For the sender-based case, the
messages and their data format will be defined here in future
versions of this document. We envision that an initialization
message from the sender to the receiver could specify which key
metrics are requested out of a possibly extensible set (pkt_loss,
var_est, skew_est, freq_est). The grouping algorithm described in
this document requires all four of these metrics, and receivers MUST
be able to provide them, but future algorithms may be able to exploit
other metrics (e.g. metrics based on explicit network signals).
Moreover, the initialization message could specify T, N, and the
necessary resolution and precision (number of bits per field).
3.1. Key metrics and their calculation
Measurements are calculated over a base interval, T. T should be long
enough to provide enough samples for a good estimate of skewness, but
short enough so that a measure of the oscillation can be made from N
of these estimates. Reference [Hayes-LCN14] uses T = 350ms and
N=M=50, which are values that seem to work well over a wide range of
practical Internet conditions.
3.1.1. Mean delay
The mean delay is not a useful signal for comparisons between flows
since flows may traverse quite different paths and clocks will not
necessarily be synchronized. However, it is a base measure for the 3
summary statistics. The mean delay, E_T(OWD), is the average one way
delay measured over T.
To facilitate the other calculations, the last N E_T(OWD) values will
need to be stored in a cyclic buffer along with the moving average of
E_T(OWD):
mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
where M <= N. Generally M=N, setting M to be less than N allows the
mechanism to be more responsive to changes, but potentially at the
expense of a higher error rate (see Section 3.4 for a discussion on
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improving the responsiveness of the mechanism.)
3.1.2. Skewness Estimate
Skewness is difficult to calculate efficiently and accurately.
Ideally it should be calculated over the entire period (M * T) from
the mean OWD over that period. However this would require storing
every delay measurement over the period. Instead, an estimate is
made over T using the previous calculation of mean_delay.
Comparisons are made using the mean of M skew estimates (an
alternative that removes bias in the mean is given in Section 3.3.3).
The skewness is estimated using two counters, counting the number of
one way delay samples (OWD) above and below the mean:
skew_est_T = (sum_T(OWD < mean_delay)
- sum_T(OWD > mean_delay)) / num_T(OWD)
where
if (OWD < mean_delay) 1 else 0
if (OWD > mean_delay) 1 else 0
skew_est_T is a number between -1 and 1
skew_est = E_M(skew_est_T) = sum_M(skew_est_T) / M
For implementation ease, mean_delay does not include the mean of the
current T interval.
Note: Care must be taken when implementing the comparisons to ensure
that rounding does not bias skew_est. It is important that the mean
is calculated with a higher precision than the samples.
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3.1.3. Variance Estimate
Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as
an estimator of the variance of the delay signal. We define PDV as
follows:
PDV = PDV_max = max_T(OWD) - E_T(OWD)
var_est = E_M(PDV) = sum_M(PDV) / M
This modifies PDV as outlined in [RFC5481] to provide a summary
statistic version that best aids the grouping decisions of the
algorithm (see [Hayes-LCN14] section IVB).
The use of PDV = PDV_min = E_T(OWD) - min_T(OWD) is currently being
investigated as an alternative that is less sensitive to noise. The
drawback of using PDV_min is that it does not distinguish between
groups of flows with similar values of skew_est as well as PDV_max
(see [Hayes-LCN14] section IVB).
3.1.4. Oscillation Estimate
An estimate of the low frequency oscillation of the delay signal is
calculated by counting and normalising the significant mean,
E_T(OWD), crossings of mean_delay:
freq_est = number_of_crossings / N
Where
we define a significant mean crossing as a crossing that
extends p_v * var_est from mean_delay. In our experiments we
have found that p_v = 0.2 is a good value.
Freq_est is a number between 0 and 1. Freq_est can be approximated
incrementally as follows:
With each new calculation of E_T(OWD) a decision is made as to
whether this value of E_T(OWD) significantly crosses the current
long term mean, mean_delay, with respect to the previous
significant mean crossing.
A cyclic buffer, last_N_crossings, records a 1 if there is a
significant mean crossing, otherwise a 0.
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The counter, number_of_crossings, is incremented when there is a
significant mean crossing and subtracted from when a non-zero
value is removed from the last_N_crossings.
This approximation of freq_est was not used in [Hayes-LCN14], which
calculated freq_est every T using the current E_N(E_T(OWD)). Our
tests show that this approximation of freq_est yields results that
are almost identical to when the full calculation is performed every
T.
3.1.5. Packet loss
The proportion of packets lost is used as a supplementary measure:
pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
Note: When pkt_loss is small it is very variable, however, when
pkt_loss is high it becomes a stable measure for making grouping
decisions.
3.2. Flow Grouping
3.2.1. Flow Grouping Algorithm
The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
context and is sufficient and efficient for small to moderate numbers
of flows. For very large numbers of flows (e.g. hundreds), a more
complex clustering algorithm may be substituted.
Since no single metric is precise enough to group flows (due to
noise), the algorithm uses multiple metrics. Each metric offers a
different "view" of the bottleneck link characteristics, and used
together they enable a more precise grouping of flows than would
otherwise be possible.
Flows determined to be experiencing congestion are successively
divided into groups based on freq_est, var_est, and skew_est.
The first step is to determine which flows are experiencing
congestion. This is important, since if a flow is not experiencing
congestion its delay based metrics will not describe the bottleneck,
but the "noise" from the rest of the path. Skewness, with proportion
of packets loss as a supplementary measure, is used to do this:
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1. Grouping will be performed on flows where:
skew_est < c_s
|| ( skew_est < c_h && PC )
|| pkt_loss > p_l
The parameter c_s controls how sensitive the mechanism is in
detecting congestion. C_s = 0.0 was used in [Hayes-LCN14]. A value
of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a little
less sensitive. C_h controls the hysteresis on flows that were
grouped as experiencing congestion last time.
These flows, flows experiencing congestion, are then progressively
divided into groups based on the freq_est, PDV, and skew_est summary
statistics. The process proceeds according to the following steps:
2. Group flows whose difference in sorted freq_est is less than a
threshold:
diff(freq_est) < p_f
3. Group flows whose difference in sorted E_N(PDV) (highest to
lowest) is less than a threshold:
diff(var_est) < (p_pdv * var_est)
The threshold, (p_pdv * var_est), is with respect to the highest
value in the difference.
4. Group flows whose difference in sorted skew_est or pkt_loss is
less than a threshold:
if pkt_loss < p_l
diff(skew_est) < p_s
otherwise
diff(pkt_loss) < (p_d * pkt_loss)
The threshold, (p_d * pkt_loss), is with respect to the
highest value in the difference.
This procedure involves sorting estimates from highest to lowest. It
is simple to implement, and efficient for small numbers of flows,
such as are expected in RTCWEB.
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3.2.2. Using the flow group signal
A grouping decisions is made every T from the second T, though they
will not attain their full design accuracy until after the N'th T
interval.
Network conditions, and even the congestion controllers, can cause
bottlenecks to fluctuate. A coupled congestion controller MAY decide
only to couple groups that remain stable, say grouped together 90% of
the time, depending on its objectives. Recommendations concerning
this are beyond the scope of this draft and will be specific to the
coupled congestion controllers objectives.
3.3. Removing Noise from the Estimates
The following describe small changes to the calculation of the key
metrics that help remove noise from them. Currently these "tweaks"
are described separately to keep the main description succinct. In
future revisions of the draft these enhancements may replace the
original key metric calculations.
3.3.1. Oscillation noise
When a path has no congestion, the PDV will be very small and the
recorded significant mean crossings will be the result of path noise.
Thus up to N-1 meaningless mean crossings can be a source of error at
the point a link becomes a bottleneck and flows traversing it begin
to be grouped.
To remove this source of noise from freq_est:
1. Set the current PDV to PDV = NaN (a value representing an invalid
record, ie Not a Number) for flows that are deemed to not be
experiencing congestion by the first skew_est based grouping test
(see Section 3.2.1).
2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV)
3. For freq_est, only record a significant mean crossing if flow is
experiencing congestion.
These three changes will remove the non-congestion noise from
freq_est.
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3.3.2. Clock drift
Generally sender and receiver clock drift will be too small to cause
significant errors in the estimators. Skew_est is most sensitive to
this type of noise. In circumstances where clock drift is high,
making M < N can reduce this error.
A better method is to estimate the effect the clock drift is having
on the E_N(E_T(OWD)), and then adjust mean_delay accordingly. A
simple method of doing this follows:
First divide the N E_T(OWD) values into two halves (N/2 in each)
-- old and new.
Calculate a mean of the old half:
Older_mean = E_old(E_T(OWD)) / N/2
Calculate a mean of the new (most recent) half:
Newer_mean = E_new(E_T(OWD)) / N/2
A linear estimate of the Clock Drift per T estimates is:
CD_T = (Newer_mean - Older_mean)/N/2
An adjusted mean estimate then is:
mean_delay = CD_Adj(E_M(E_T(OWD))) = E_M(E_T(OWD)) + CD_T * M/2
CD_Adj can be thought of as a prediction of what the long term mean
will be in the current measurement period T. It is used as the basis
for skew_est and freq_est.
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3.3.3. Bias in the skewness measure
If successive calculations of skew_est are made with very different
numbers of samples (num_T(OWD)), the simple calculation of
E_M(skew_est) used for grouping decisions will be biased by the
intervals that have few samples samples. This bias can be corrected
if necessary as follows.
skew_base_T = (sum_T(OWD < mean_delay
- sum_T(OWD > mean_delay)
skew_est = sum_MT(skew_base_T)/num_MT(OWD)
This calculation requires slightly more state, since an
implementation will need to maintain two cyclic buffers storing
skew_base_T and num_T(OWD) respectively to manage the rolling
summations (note only one cyclic buffer is needed for the calculation
of skew_est outlined previously).
3.4. Reducing lag and Improving Responsiveness
Measurement based shared bottleneck detection makes decisions in the
present based on what has been measured in the past. This means that
there is always a lag in responding to changing conditions. This
mechanism is based on summary statistics taken over (N*T) seconds.
This mechanism can be made more responsive to changing conditions by:
1. Reducing N and/or M -- but at the expense of less accurate
metrics, and/or
2. Exploiting the fact that more recent measurements are more
valuable than older measurements and weighting them accordingly.
Although more recent measurements are more valuable, older
measurements are still needed to gain an accurate estimate of the
distribution descriptor we are measuring. Unfortunately, the simple
exponentially weighted moving average weights drop off too quickly
for our requirements and have an infinite tail. A simple linearly
declining weighted moving average also does not provide enough weight
to the most recent measurements. We propose a piecewise linear
distribution of weights, such that the first section (samples 1:F) is
flat as in a simple moving average, and the second section (samples
F+1:M) is linearly declining weights to the end of the averaging
window. We choose integer weights, which allows incremental
calculation without introducing rounding errors.
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3.4.1. Improving the response of the skewness estimate
The weighted moving average for skew_est, based on skew_est in
Section 3.3.3, can be calculated as follows:
skew_est = ((M-F+1)*sum(skew_base_T(1:F))
+ sum([(M-F):1].*skew_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F))
+ sum([(M-F):1].*numsampT(F+1:M)))
where numsampT is an array of the number of OWD samples in each T (ie
num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1) is
the most recent calculation of skew_base_T; 1:F refers to the integer
values 1 through to F, and [(M-F):1] refers to an array of the
integer values (M-F) declining through to 1; and ".*" is the array
scalar dot product operator.
3.4.2. Improving the response of the variance estimate
The weighted moving average for var_est can be calculated as follows:
var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M)))
/ (F*(M-F+1) + sum([(M-F):1])
where 1:F refers to the integer values 1 through to F, and [(M-F):1]
refers to an array of the integer values (M-F) declining through to
1; and ".*" is the array scalar dot product operator. When removing
oscillation noise (see Section 3.3.1) this calculation must be
adjusted to allow for invalid PDV records.
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4. Measuring OWD
This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks.
The SBD mechanism described in this draft relies on differences
between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all
summary statistics are relative to the mean OWD and sender/receiver
clock offsets should be approximately constant over the measurement
periods, the offset is subtracted out in the calculation.
4.1. Time stamp resolution
The SBD mechanism requires timing information precise enough to be
able to make comparisons. As a rule of thumb, the time resolution
should be less than one hundredth of a typical path's range of
delays. In general, the lower the time resolution, the more care
that needs to be taken to ensure rounding errors do not bias the
skewness calculation.
Typical RTP media flows use sub-millisecond timers, which should be
adequate in most situations.
5. Acknowledgements
This work was part-funded by the European Community under its Seventh
Framework Programme through the Reducing Internet Transport Latency
(RITE) project (ICT-317700). The views expressed are solely those of
the authors.
6. IANA Considerations
This memo includes no request to IANA.
7. Security Considerations
The security considerations of RFC 3550 [RFC3550], RFC 4585
[RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Non-authenticated RTCP packets carrying shared bottleneck indications
and summary statistics could allow attackers to alter the bottleneck
sharing characteristics for private gain or disruption of other
parties communication.
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8. Change history
Changes made to this document:
01->02 : New section describing improvements to the key metric
calculations that help to remove noise, bias, and
reduce lag. Some revisions to the notation to make
it clearer. Some tightening of the thresholds.
00->01 : Revisions to terminology for clarity
9. References
9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
9.2. Informative References
[Hayes-LCN14]
Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
Shared Bottleneck Detection using Shape Summary
Statistics", Proc. the IEEE Local Computer Networks
(LCN) p150-158, September 2014, <http://heim.ifi.uio.no/
davihay/
hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
[I-D.welzl-rmcat-coupled-cc]
Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
control for RTP media", draft-welzl-rmcat-coupled-cc-04
(work in progress), October 2014.
[ITU-Y1540]
ITU-T, "Internet Protocol Data Communication Service - IP
Packet Transfer and Availability Performance Parameters",
Series Y: Global Information Infrastructure, Internet
Protocol Aspects and Next-Generation Networks ,
March 2011,
<http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, July 2003.
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Real-time Transport Control
Hayes, et al. Expires September 4, 2015 [Page 17]
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Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
July 2006.
[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
Real-time Transport Control Protocol (RTCP)-Based Feedback
(RTP/SAVPF)", RFC 5124, February 2008.
[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
Applicability Statement", RFC 5481, March 2009.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
December 2012.
Authors' Addresses
David Hayes (editor)
University of Oslo
PO Box 1080 Blindern
Oslo, N-0316
Norway
Phone: +47 2284 5566
Email: davihay@ifi.uio.no
Simone Ferlin
Simula Research Laboratory
P.O.Box 134
Lysaker, 1325
Norway
Phone: +47 4072 0702
Email: ferlin@simula.no
Michael Welzl
University of Oslo
PO Box 1080 Blindern
Oslo, N-0316
Norway
Phone: +47 2285 2420
Email: michawe@ifi.uio.no
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