One document matched: draft-huang-xrblock-rtcweb-rtcp-xr-metrics-03.txt
Differences from draft-huang-xrblock-rtcweb-rtcp-xr-metrics-02.txt
Network Working Group R. Huang
INTERNET-DRAFT R. Even
Intended Status: Informational Huawei
Expires: August 18, 2014 V. Singh
Aalto University
D. Romascanu
Avaya
L. Deng
China Mobile
February 14, 2014
Considerations for Selecting RTCP Extended Report (XR)
Metrics for the RTCWEB Statistics API
draft-huang-xrblock-rtcweb-rtcp-xr-metrics-03
Abstract
This document describes monitoring features related to RTCWEB. It
provides a list of RTCP XR metrics that are useful and may need to be
supported in some RTCWEB implementations.
Status of this Memo
This Internet-Draft is submitted to IETF in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF), its areas, and its working groups. Note that
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Copyright and License Notice
Copyright (c) 2014 IETF Trust and the persons identified as the
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document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
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 RTP Statistics in WebRTC Implementations . . . . . . . . . . . 3
4 Considerations for Impact of Measurement Interval . . . . . . . 4
5 Candidate Metrics . . . . . . . . . . . . . . . . . . . . . . . 4
5.1 Network Impact Metrics . . . . . . . . . . . . . . . . . . 5
5.1.1 Loss and Discard Packet Count Metric . . . . . . . . . 5
5.1.2 Burst/Gap Pattern Metrics for Loss and Discard . . . . 6
5.1.3 Run Length Encoded Metrics for Loss, Discard . . . . . 6
5.1.3 ECN related Metrics . . . . . . . . . . . . . . . . . . 7
5.2 Application Impact Metrics . . . . . . . . . . . . . . . . 7
5.2.1 Discard Octets Metric . . . . . . . . . . . . . . . . . 7
5.2.2 Frame Impairment Summary Metrics . . . . . . . . . . . 8
5.2.3 Jitter Buffer Metrics . . . . . . . . . . . . . . . . . 8
5.3 Recovery metrics . . . . . . . . . . . . . . . . . . . . . 9
5.3.1 Post-repair Packet Count Metrics . . . . . . . . . . . 9
5.3.2 Run Length Encoded Metric for Post-repair . . . . . . . 9
6 Security Considerations . . . . . . . . . . . . . . . . . . . . 9
7 IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 10
8 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . 10
9 References . . . . . . . . . . . . . . . . . . . . . . . . . . 10
9.1 Normative References . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 12
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1 Introduction
Web based real-time communication (WebRTC) is becoming prevalent. To
help measure the quality of the WebRTC services better, applications
need to be able to estimate the service quality and to inform the
application about network problems. If sufficient information
(metrics or statistics) is provided to the applications, it can
function better in providing better media quality. [RTCWEB-REQ]
specifies a requirement for statistics, which is listed below for
convenient reading.
"F38 The browser MUST be able to collect statistics, related to
the transport of audio and video between peers, needed to estimate
quality of service."
[RTCWEB-STAT] describes a registration procedure for choosing metrics
reported by the JavaScript API. It also identifies basic metrics
reported in the RTCP Sender and Receiver Report (SR/RR) to fulfill
this requirement. These basic metrics from RTCP SR/RR may not be
sufficient for precise quality monitoring or troubleshooting. They
are better to be complemented with correspondent metrics defined in
RTCP XR. Thus, indicating a minimum set of additional statistic
metrics would be helpful. In this document, we provide some
guidelines on what kind of metrics should be chosen to complement the
metrics from basic RTCP SR/RR specified in [RTCWEB-STAT].
2 Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
3 RTP Statistics in WebRTC Implementations
Statistics are generated in browsers in WebRTC implementations. They
could be divided into 3 categories: stream level, session level and
other aspects. RTP statistics are usually stream level as they are
always assigned with corresponding SSRCs, and they could be generated
locally or remotely. For those remote RTP statistics, local browsers
need standard way to get the information from the other side.
[RTCWEB-STAT] defines some RTP statistics generated locally or
obtained from the remote side using standard RTCP SR/RR periodically.
They are SentPacketCount, SentOctetCount, packetsLost, Jitter,
ReceivedPacketCount, ReceivedOctetCount. However, these only provides
partial and limited information, which may not be sufficient for
diagnosing problems or quality monitoring, example is in Section
5.1.1. RTP Control Protocol Extended Reports [RFC3611] and other
extensions discussed in XRBLOCK working group have defined more
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statistics complementing those in RTCP SR/RR. Some of these extended
metrics should be considered to use in WebRTC implementations.
Section 5 presents some detail discussions on the use of metrics.
However, RTCP XR are not mandated in RTCWEB because some of the
information conveyed by RTCP XR is highly application dependent. at
the current stage, we only consider those extended statistics
measured at a local endpoint and useful for a range of WebRTC
implementations. RTCP XR may be supported after a successful SDP
negotiation between browsers, thereafter the application has access
to both local and remote statistics.
Statistics can be extracted by the application from the local browser
by JavaScript applications using an API. Once the JavaScript
application gets this information, they can report it to application
servers or 3rd party monitoring systems, which provide quality
estimations or diagnosis services for application developers. The
endpoint may use some standard protocol e.g., RTCP XR, or a private
protocol to send the metrics to a measurement server. But this part
is outside the scope of this memo, also outside the scope of RTCWEB,
and will not be discussed in more detail.
4 Considerations for Impact of Measurement Interval
RTCP extensions like RTCP XR usually share the same timing interval
with RTCP SR/RR, i.e., they are sent as compound packets, together
with the RTCP SR/RR. Alternatively, the RTCP XR can use a different
measurement interval and all XRs using the same measurement interval
are compounded together and the measurement interval is indicated in
a specific measurement information block [RFC6776].
When using WebRTC Statistics APIs (see section 7 of [WebRTCAPI]),
JavaScript applications can query this information at arbitrary
intervals. Some applications may choose 1 second or another interval.
Statistics generated remotely, e.g. those conveyed by RTCP SR/RR,
won't change until the next RTCP interval, even if JavaScript
applications query it at a very small interval. But those generated
locally, e.g. statistics suggested in this memo, have no such
limitation.
5 Candidate Metrics
Since following metrics are all defined in RTCP XR which is not
mandated in WebRTC, all of them are local. However, if RTCP XR is
supported by negotiation between two browsers, following metrics can
also be generated remotely and be sent to local by RTCP XR packets.
Following metrics are classified into 3 categories: network impact
metrics, application impact metrics and recovery metrics. Network
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impact metrics are the statistics recording the information only for
network transmission. They are useful for network problem diagnosis.
Application impact metrics mainly collect the information in the
viewpoint of application, e.g., bitrate, frames rate or jitter
buffers. Recovery metrics reflect how well the repair mechanisms,
e.g. loss concealment, retransmission or FEC, perform. All of the 3
types of metrics are useful for quality estimations of services in
WebRTC implementations. JavaScript application developers could use
these metrics to better calculate MoS values or Media Delivery Index
(MDI) for their services.
5.1 Network Impact Metrics
5.1.1 Loss and Discard Packet Count Metric
In multimedia transport, packets that are received abnormally are
classified into 3 types: lost, discarded and duplicate packets.
Packet loss may be caused by network devices breakdown, bit-error
corruption or serious congestions (packets dropped by an intermediate
router queue). Duplicated data packets may be due to network delays
which causes the sender to retransmit the original packets. Discarded
packets are packets that have been delayed long enough and are
considered useless by the receiver. Lost and discarded packets cause
problems for multimedia services, as missing data and long delay can
cause degradation in service quality, e.g., missing large blocks of
contiguous packets (lost or discarded) may cause choppy audio, and
long network transmission delay time may cause audio or video
buffering. RTCP SR/RR defines a metric for counting the total number
of RTP data packets that have been lost since the beginning of
reception. But this statistic doesn't distinguish lost packets from
discarded and duplicate packets. Packets that arrive late and are
discarded are not treated as lost, and duplicate packets will be
regarded as a normally received packet. This metric is misleading if
many duplicate packets are received or packets discarded, which
causes the quality of media transport to look okay from the statistic
while actually users are experiencing bad service quality, because
packets are still missing. So in such cases, it's better to use more
accurate metrics in addition to those defined in RTCP SR/RR.
The lost packets and duplicated packets metrics defined in Statistics
Summary Report Block of [RFC3611] extend the information of loss
carried in standard RTCP SR/RR. They explicitly give an account of
lost and duplicated packets. Lost packets counts are useful for
network problem diagnosis. It's better to use the loss packets
metrics of [RFC3611] to indicated the packet lost counting instead of
the cumulative number of packets lost metric of [RFC3550]. Duplicated
packets are usually rare and have little effect on QoS evaluation. So
it is not suitable to be used in WebRTC.
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Using loss metrics without considering discard metrics may result in
inaccurate quality evaluation, as packet discard due to jitter is
often more prevalent than packet loss in modern IP networks. The
discarded metric specified in [RFC7002] counts the number of packets
discarded due to the jitter. It augments the loss statistics metrics
specified in standard RTCP SR/RR. For those RTCWEB services with
jitter buffer requiring precise quality evaluation and accurate
troubleshooting, this metric is useful as a complement to the metrics
of RTCP SR/RR.
5.1.2 Burst/Gap Pattern Metrics for Loss and Discard
RTCP SR/RR defines coarse metrics regarding loss statistics, the
metrics are all about per call statistics and not detailed enough to
capture some transitory nature of the impairments like bursty packet
loss. Even if the average packet loss rate is low, the lost packets
may occur during short dense periods, resulting in short periods of
degraded quality. Distributed burst provides a higher subjective
quality than a non-burst distribution for low packet loss rates
whereas for high packet loss rates the converse is true. So capturing
burst gap information is very helpful for quality evaluation and
locating impairments. If RTCWEB services have the requirement to
evaluate the services quality, burst gap metrics provides more
accurate information than RTCP SR/RR.
[RFC3611] introduces burst gap metrics in VoIP report block. These
metrics record the density and duration of burst and gap periods,
which are helpful in isolating network problems since bursts
correspond to periods of time during which the packet loss/discard
rate is high enough to produce noticeable degradation in audio or
video quality. Burst gap related metrics are also introduced in
[RFC7003] and [RFC6958] which define two new report blocks for usage
in a range of RTP applications beyond those described in RFC3611.
These metrics distinguish discarded packets from loss packets that
occur in the bursts period and provides more information for
diagnosing network problems. Besides that, the metric number of
bursts counts the burst events which could provide useful information
to evaluate the frequency of burst occurrences. So if WebRTC services
have the requirement to do quality evaluation and observe when and
why quality degrades, these metrics should be considered.
5.1.3 Run Length Encoded Metrics for Loss, Discard
Run-length encoding uses a bit vector to encode information about the
packet. Each bit in the vector represents a packet and depending on
the signaled metric it defines if the packet was lost, duplicated,
discarded, or repaired. An endpoint typically uses the run length
encoding to accurately communicate the status of each packet in the
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interval to the other endpoint. [RFC3611], [XRBLOCK-DISCARDRLE]
define run-length encoding for lost and duplicate packets, discarded
packets.
For WebRTC, the application could benefit from the additional
information. If losses occur after discards, an endpoint may be able
to correlate the two run length vectors to identify congestion-
related losses, i.e., a router queue became overloaded causing delays
and then overflowed. If the losses are independent, it may indicate
bit-error corruption. In the case of RTCP XR supported, this type of
metrics are not suggested to use due to their enormous amount of
data. Whereas, for local case, it's fine to have them.
5.1.3 ECN related Metrics
ECN can be used to minimize the impact of congestion on real-time
multimedia traffic. The use of ECN provides a way for the network to
send congestion control signals to the media transport without having
to impair the media. Unlike packet loss, ECN signals unambiguously
indicate congestion to the transport as quickly as feedback delays
allow and without confusing congestion with losses that might have
occurred for other reasons such as transmission errors.
ECN related metrics, such as ECN-CE Counter found in [RFC6679], could
be used to show the cumulative number of RTP packets received from
this SSRC since the receiver joined the RTP session that were ECN-CE
marked, including ECN-CE marks in any duplicate packets. It is useful
for detecting network congestion status before the actual packet loss
occurs. Media senders can control how they reduce their transmission
rate and hence media quality, rather than responding to and trying to
conceal the effects of unpredictable packet loss.
It is hence recommended that ECN related metrics (either ECN Feedback
Report and ECN Summary Report) be considered for RTCWEB applications
in ECN-enabled networks. The definition of these metrics could be
found in [RFC6679].
5.2 Application Impact Metrics
5.2.1 Discard Octets Metric
The metric reports the cumulative size of the packets discarded in
the interval, it is complementary to number of discarded packets. An
application measures sent octets and received octets to calculate
sending rate and receiving rate, respectively. The application can
calculate the actual bitrate in a particular interval by subtracting
the discarded octets from the received octets.
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For WebRTC, discarded octets supplements the sent and received octets
and provides an accurate method for calculating the actual bitrate
which is an important parameter to reflect the quality of the media.
The discarded bytes metric is defined in [XRBLOCK-DISCARDBYTES].
5.2.2 Frame Impairment Summary Metrics
RTP has different framing mechanisms for different payload types. For
audio streams, a single RTP packet may contain one or multiple audio
frames, each of which has a fixed length. On the other hand, in video
streams, a single video frame may be transmitted in multiple RTP
packets. The size of each packet is limited by the Maximum
Transmission Unit (MTU) of the underlying network. However,
statistics from standard SR/RR only collect information from
transport layer, which may not fully reflect the quality observed by
the application. Video is typically encoded using two frame types
i.e., key frames and derived frames. Key frames are normally just
spatially compressed, i.e., without prediction from other pictures.
The derived frames are temporally compressed, i.e., depend on the key
frame for decoding. Hence, Key frames are much larger in size than
derived frames. The loss of these key frames results in a substantial
reduction in video quality. Thus it is meaningful to consider this
application layer information in WebRTC implementations, which
influence sender strategies to mitigate the problem or require the
accurate assessment of users' quality of experience.
The following metrics can also be considered for WebRTC's Statistics
API: number of discarded key frames, number of lost key frames,
number of discarded derived frames, number of lost derived frames.
These metrics could be used to calculate Media Loss Rate (MLR) of
MDI. Details of the definition of these metrics are in [RFC7003].
Besides these, the metric, number of frames, should also be
considered since frame rate, an important parameter for quality
estimation, could be calculated from it.
5.2.3 Jitter Buffer Metrics
The size of the jitter buffer affects the end-to-end delay on the
network and also the packet discard rate. When the buffer size is too
small, slower packets are not played out and dropped, while when the
buffer size is too large, packets are held longer than necessary and
consequently reduce conversational quality. Measurement of jitter
buffer should not be ignored in the evaluation of end user perception
of conversational quality. Jitter buffer related metrics, such as
maximum and nominal jitter buffer, could be used to show how the
jitter buffer behaves at the receiving end of RTP stream. They are
useful for providing better end-user quality of experience (QoE) when
jitter buffer factors are used as inputs to calculate MoS values.
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Thus for those cases, jitter buffer metrics could be considered. The
definition of these metrics could be found in [RFC7005].
5.3 Recovery metrics
5.3.1 Post-repair Packet Count Metrics
Error-resilience mechanisms, like RTP retransmission or FEC, are
optional in RTCWEB because the overhead of the repair bits adding to
the original streams. But they do help to greatly reduce the impact
of packet loss and enhance the quality of transmission. Web
applications could support certain repair mechanism after negotiation
between both sides of browsers when needed. For these web
applications using repair mechanisms, providing some statistic
information for the performance of their repair mechanisms could help
to have a more accurate quality evaluation.
The un-repaired packets count and repaired loss count defined in
[XRBLOCK-PRCOUNT] provide the recovery information of the error-
resilience mechanisms to the monitoring application or the sending
endpoint. The endpoint can use these metrics to ascertain the ratio
of repaired packets to lost packets. Including this kind of metrics
helps the application evaluate the effectiveness of the applied
repair mechanisms.
5.3.2 Run Length Encoded Metric for Post-repair
[RFC5725] defines run-length encoding for post-repair packets. When
using error-resilience mechanisms, the endpoint can correlate the
loss run length with this metric to ascertain where the losses and
repairs occurred in the interval. This provides more accurate
information for recovery mechanisms evaluation than those in Section
5.3.1. However, it is not suggested to use due to their enormous
amount of data when RTCP XR are supported.
For WebRTC, the application may benefit from the additional
information. If losses occur after discards, an endpoint may be able
to correlate the two run length vectors to identify congestion-
related losses, i.e., a router queue became overloaded causing
delays and then overflowed. If the losses are independent, it may
indicate bit-error corruption. Lastly, when using error-resilience
mechanisms, the endpoint can correlate the loss and post-repair run
lengths to ascertain where the losses and repairs occurred in the
interval. For example, consecutive losses are likely not to be
repaired by a simple FEC scheme.
6 Security Considerations
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The monitoring activities are implemented between two browsers or
browser-to-server. Also encryption procedures, such as those being
suggested for a Secure RTCP (SRTCP), can be used. It is believed
that monitoring in RTCWEB introduces no new security considerations
beyond those described in [RTCWEB-RTPUSAGE] and [RTCWEB-SECURITY].
7 IANA Considerations
There is no IANA action in this document.
8 Acknowledgement
The authors would like to thank Colin Perkins, Al Morton, and Shida
Schubert for their valuable comments and suggestions on this
document.
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.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, July 2003.
[RFC3611] Friedman, T., Ed., Caceres, R., Ed., and A. Clark, Ed.,
"RTP Control Protocol Extended Reports (RTCP XR)",
RFC 3611, November 2003.
[RTCWEB-REQ] Holmberg, C., Hakansson, S., and G. Eriksson, "Web Real-
Time Communication Use-cases and Requirements", I-D.ietf-
rtcweb-use-cases-and-requirements, December 2012.
[RTCWEB-STAT] Alvestrand, H., "A Registry for WebRTC statistics
identifiers", I-D.alvestrand-rtcweb-stats-registry,
September 24, 2012.
[RTCWEB-RTPUSAGE] Perkins, C., Westerlund, M., and J. Ott, "Web Real-
Time Communication (WebRTC): Media Transport and Use of
RTP", I-D.ietf-rtcweb-rtp-usage, February 2013.
[RTCWEB-SECURITY] Rescorla, E., "Security Considerations for RTC-
Web", I-D.ietf-rtcweb-security, January 2013.
[RFC6679] Westerlund, M., Johansson, I., Perkins, C., O'Hanlon, P.,
and K. Carlberg, "Explicit Congestion Notification (ECN)
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for RTP over UDP", RFC 6679, August 2012.
[RFC6792] Wu, Q., Ed., Hunt, G., and P. Arden, "Monitoring
Architecture for RTP", RFC 6792, November 2012.
[RFC7002] Hunt, G., Clark, A., Zorn, G., and Q. Wu, "RTCP XR Report
Block for Discard Metric Reporting", RFC 7002, September
2013.
[XRBLOCK-DISCARDBYTES] Singh, V., Ott, J., Curcio, I.D.D., "RTP
Control Protocol (RTCP) Extended Reports (XR) for Bytes
Discarded Metric", I-D.ietf-xrblock-rtcp-xr-bytes-
discarded-metric, October 2013
[XRBLOCK-PRCOUNT] Huang, R., Singh, V., "RTP Control Protocol (RTCP)
Extended Report (XR) for Post-Repair Non-Run Length
Encoding (RLE) Loss Count Metrics", I-D.huang-xrblock-
post-repair-loss-count, September 2013.
[RFC7003] Hunt, G., Clark, A., and Q. Wu, Ed., "RTCP XR Report Block
for Burst/Gap Discard Metric Reporting", RFC 7003,
September 2013.
[RFC6958] Hunt, G., Clark, A., Zhang, S., Ed., "RTCP XR Report Block
for Burst/Gap Loss Metric Reporting", RFC 6958, April
2013.
[XRBLOCK-DISCARDRLE] Singh, V., Ott, J., Curcio, I.D.D., "RTP Control
Protocol (RTCP) Extended Reports (XR) for Run Length
Encoding (RLE) of Discarded Packets", I-D.ietf-xrblock-
discard-rle-metrics, October 2013.
[RFC5725] Begen, A., Hsu, D., Lague, M., "Post-Repair Loss RLE
Report Block Type for RTP Control Protocol (RTCP) Extended
Reports (XRs)", February 2010
[RFC7005] Clark, A., Singh, V., and Q. Wu, "RTCP XR Report Block for
Jitter Buffer Metric Reporting", RFC 7005, September 2013
[RFC6798] Clark, A., Wu, Q., Ed., "RTCP Control Protocol (RTCP)
Extended Report (XR) Block for Packet Delay Variation
Metric Reporting", November 2012.
[RFC7003] Zorn, G., Schott, R., Wu, Q., Huang, R., "RTP Control
Protocol (RTCP) Extended Report (XR) Blocks for Summary
Statistics Metrics Reporting", September 2013
[WebRTCAPI] Bergkvist, A., Burnett, D., Jennings, C., Ed.,
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http://dev.w3.org/2011/webrtc/editor/webrtc.html, June
2013
Authors' Addresses
Rachel Huang
Huawei
101 Software Avenue, Yuhua District
Nanjing 210012
China
EMail: rachel.huang@huawei.com
Roni Even
Huawei
14 David Hamelech
Tel Aviv 64953
IsraelOctober 11, 2013October 11, 2013
EMail: roni.even@mail01.huawei.com
Varun Singh
Aalto University
School of Electrical Engineering
Otakaari 5 A
Espoo, FIN 02150
Finland
Email: varun@comnet.tkk.fi
URI: http://www.netlab.tkk.fi/~varun/
Dan Romascanu
Avaya
Email: dromasca@avaya.com
Lingli Deng
China Mobile
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Email: denglingli@chinamobile.com
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