Lecture 7. P2P streaming: design aspects and performance issues

1. Lecture 7. P2P streaming: design aspects and performance issues Dmitri Moltchanov, TUT, Fall 2015

2. Outline • Quality: compression basics • How to assess video quality? • Recent observations of live streaming systems • Improving streaming performance in P2P systems • Overlay architectures • Layered coding • Multiple description coding • Scheduling • Incentive mechanisms • Traffic localization • Active quality control?

3. The main difference • Scheduling of chunks • Why? Different quality metrics! • Recall BitTorrent • Rarest chunk first • Tit-for-tat • Improve network wide dissemination first • P2P streaming • Scheduling window • Next chunk first • Improve my own performance first

4. Compression basics

5. Performance degradation • Network transmission • Losses • Delays • Well, what’s about compression? • Highly desirable/undesirable for network environment • Desirable: reduces required rate up to 20 times • Undesirable • Degrades the quality (lossy compression) • Magnifies the effect of losses

6. Video compression standards • Video: two or more media Mx-ed streams • Video stream • Audio stream (voice as a special case) • Additional streams, e.g. subtitles, different languages, etc. • Standards • ITU-T Video Coding Experts Group (VCEQ): H.26x • ISO/IEC Moving Pictures Experts Group (MPEG): MPEG-1/2/4/7 • What do these standards provide • Analog/digital digital/analog conversion • Lossy compression of media • Stream description • Packetization

7. How compression is achieved • Compression removes the following redundancies • Spatial • Temporal • Psycho-visual • We exploit them by • Predicting pixel values in space • Predicting them in time • Loosing some unnecessary info • Compression: up to 30 times

8. Types of frames • Three types of frames • I-frame (intracoded) • P-frame (predicted) • B-frames (bi-directionally predicted) • Frames can be organized in a group of pictures (GoP) • A repeated sequence of frames • Why? Access unit for players • Example: (3,12), 3 distance between I and P, 12 length of GoP

9. Temporal prediction • Removing temporal redundancy • Works with digitized macroblocks

10. A problem with prediction • Recall transmission media for a moment • Loss of a packet • Error propagation in time • Error propagation in space

11. Assessing video quality

12. Metrics of interest • Metrics we care here • Service integrity • Audiovisual quality • Service integrity • Continuity of playback • Affected by system topology, network performance • Prefetching delay (response time) • Affected mainly by the topology • … • What quality we are talking about? A joint one • Video quality • Audio quality • Overall quality (audio+video)

13. Joint assessment of video and audio • We are talking about multimedia here • One of media may have more profound effect • Audio/video degradation do correlate • They may affect joint quality in some complex way… • How to access the quality? • Develop a new test for multimedia? • Develop audio and video separately and then combine them? • Single test for joint quality • Could be complicated • Separate ones • Easier to develop • Reuse of existing concepts • How to join together?

14. Subjective tests for video/audio • MOS: Mean opinion score • Survey humans watching your service in similar conditions • Ask them to grade it using a certain scale, e.g. 1-5 • Compute the mean… • What are the problems with MOS • Requires an audience of people • Requires very specific conditions • There are a number of standards (e.g. ITU-R BT-500.11 for video) • What’s about network environment?

15. Video: objective tests • What do they do? • Try to automate the process of quality assessment • How: try to provide good correlation with subjective tests • Simple tests: based on comparing pixel values • Complex tests: based on properties of human visual system • Types of objective tests • Based on availability of video at the evaluation point • RF: full-reference • RR: reduced-reference • NR: no-reference • Suitability for networking • NR are fully appropriate • RR are appropriate • FR are not suitable at all

16. Video: objective tests • Simple pixel-based metrics • Mean squared error (MSE) • X and Y are arrays of pixels, N is the number of pixels • Peak signal-to-noise (PSNR) • L is max possible pixel value • Some observations • Sometimes provide poor performance • But: only few advanced FR VQA techniques are better • Inherently of FR type… • Can be modified to work in network environment

17. Audio: subjective tests • First of all, what media are we talking about? • Speech? 0.3-3.4Khz • Audio? 0.02-20KHz • They are way different! • Subjective tests are MoS based • Objective tests • Similar classification NR/RR/FR • ITU-T P.862, PESQ for speech, FR type • ITU-R BS.1387, PEAQ for audio, FR type • No RR/NR tests proposed yet for generic audio • There is for speech… let us consider as an example

18. Speech: E-model • ITU-T G.107 E-model needs • Type of the codec • Network-intristic performance metrics • IP packet loss ratio (IPLR) • IP packet transfer delay (IPTD) • Measure of quality is R-factor • R0 – starting value • Is – degradation as a result of compression/coding • Id – degradation caused by end-to-end delay • Ie – degradation caused by losses • A – advantage factor

19. Speech: E-model and MoS • Does not work as expected… Why? • Packet losses tends to clip • Users differently perceives • Switching from bad to good quality seems longer that it is • Switching from good to bad is almost instantly perceived

20. Joint assessment: combining together • Non-linear model • ra, rv, rav – MOS values of audio/video/combined stream • some coefficients we need to find • rarv– responsible for cross-dependence factor • Usage of this models • You need to fit it to empirical data • Linear without cross-dependence factor rarv • Concluding notes • Most models developed for low-bit-rate low-motion video • Everything greatly depends on the type of the codec • Loss may lead to the loss of a whole frame… • Audio is often more important here (e.g. video call)…

21. Streaming quality observations

22. Biggest concerns: asymmetricity + BE • Asymmetric bandwidth • It is the nature of access/subscription (ADSL, cable models) • It is what operator wants (pays for outgoing traffic) • It is what clients sometimes offer (limit outgoing rate) • What can we do? Server farms! • Just like in CDNs… • Best-effort Internet • Quality degradation • Reasons? Losses, delays as a result of congestion • P2P may somehow decrease these effects…

23. Measurements 2007-now • Some systems may handle high peer churns effectively • Some may handle 100-200 thousands of viewers • A lot of clients get good quality • Some clients get extremely bad quality • Increase of prefetching period increases quality • Often systems require 10-100 seconds of prefetching • There is threshold not allowing further increase • Quality degradation • Picture freezing as a result of losses (problem of decoders) • Results in discontinuity which is very annoying • Mesh topologies are generally better

24. Quality monitoring system1 • Concept: crawling of buffer maps of clients • Continuous monitoring (they used it for PPLive) • No additional software • Passive sniffing • Problems? • Only one… protocols are proprietary  • Does not represent quality… Better call it starving of nodes. 1Hei et. al “Inferring network-wide quality in P2P live streaming systems”, IEEE JSAC, 25(9),1640-1654, Dec. 2007

25. Improving streaming in P2P

26. What are the means? • A plenty of! • We do not depend on the network operator! Well… • We may ask for just not disturbing us with our efforts! • Proactive ways • Overlay architecture • Layered coding • Multiple-description coding • Traffic localization • Scheduling • Incentive mechanisms • Server farms • Active way: monitoring+supporting starving peers • Well, we are not ready yet…

27. Overlay architecture • Mesh vs. tree • Comparison between these two • In trees information is disseminated faster • In meshes reliability is much better • Reliability in tree topologies • Multiple trees! • Well, multiple meshes will still be more reliable… • Sometimes referred to as PULL (mesh) and PUSH (tree)

28. Layered coding • Principals • n layers • one base layer • n-1 enhancement layers • Usage in P2P • Tree/mesh carrying base layer is for all to sign for • Other trees/meshes are just for better quality • Requirements • Trees/meshes need to enumerated • You need to follow the order of layers • ks layer if you subscribed to the previous n-k layers Two layers

29. Multiple description coding • Similar principle • n layers • one base layer • n-1 enhancement layers • layer are independent! • Similar usage in P2P • At least one tree/mesh needs to be joined • Other trees/meshes enhance quality Three layers

30. Why MDC and LC are so good? The tool behind is path diversity Excellent for P2P systems multiple trees/meshes!

31. Scheduling • Applicable to mesh systems only • File sharing P2P • File is divided into chunks • It does not matter which of those comes next • File sharing is delay tolerant • P2P streaming: scheduling window • Sequence of packets ahead of the current playback • Needs to be received to ensure continuous playback • One of the most critical parts

32. Scheduling: trade-offs • Careful tuning of the window size for two metrics • Level of dissemination of data (LOD) • Continuity of playback (COP) • Scheduling window is large • LOD increases • 1st POV: COP increases as more sources have data • 2nd POV: COP decreases as the window is larger • Scheduling window is small • LOD decreases • 1st POV: COP increases as the window is small • 2nd POV: COP decreases as less sources have data • Which of these dominates in each case? No ideas…

33. Scheduling: strategies • Scheduling strategies • Urgency-based • Rarity-based • Hybrid approaches • Urgency-based • Ask for those that are closer to the playback • Egocentric disruptive approach • LOD decreases, COP increases in the short run • Rarity-based • Ask the rarest block in the window • LOD increases, quality may increase in the long run • Hybrid approach: assign priorities to the packets

34. Incentive mechanism • Why it is important? • P2P relies on clients contributing bandwidth • Free-riding problem: decrease available bandwidth • Philosophy of incentive mechanisms • Quality needs to be proportional to the contribution of a peer • You do not contribute, you get nothing • Groups of incentive mechanisms • Monetary-based • Reciprocity-based • Effect on quality • Balance of upload and download

35. Incentive mechanism: monetary • Monetary • Virtual currency • Spent when getting, obtained when providing • Centralized mechanisms using accounting modules • Shortcomings and advantages • Additional network load • Additional timing overhead • Single point of failure • Good for commercial systems • Could be the only way for future commercial P2P systems • Real money instead of virtual currency 

36. Incentive mechanism: reciprocity • Reciprocity-based • Peers maintain info about other peers’ contributions • Distributed algorithm • Direct and indirect approaches • Direct approach • Peer uses its own history of communication • What if no such history available (e.g. new peer)? • BitTorrent-like system: free-riding is easy • Indirect approach • Peer gathers info from other peers too • Vulnerable to malicious nodes • Something needs to be done with free-riding!

37. Traffic localization • What is that? • Attempt to localize traffic within domains • Around 90% crosses inter-ISP links • Operators throttle P2P traffic: http://www.p2pon.com/guides/ • Trying to please networks operators… • Is it possible? • Comcast localized 34% in its field trials • General consensus: up to 80% can be localized • What is the effect? • Straight effect: localization • Side effect: better quality as only few long-distance connections • Side effect: high degree of localization decreases quality…

38. Traffic localization: approaches • Caching techniques • Create cache of files nearby • Where? Operators don’t want to deal with that… • Use of gateways • Needs to be very powerful nodes • Nodes could be too loosely connected • Biased choice of peers • Preference is given to local peers • How to identify local peers? • Measurements of RTT • IP addresses (prefix matching /8, /24, /13) • Special services provided by their party • e.g. Internet Coordinate Systems (ICS)

39. Server farms • What is that? • Servers injecting the bandwidth into the overlay • Can be done for VOD and live streaming • Implemented in most P2P streaming systems e.g. SopCast • Hybrid CDN/P2P systems!

40. Active quality improvement • Basic principle • Monitor quality locally • Identify moments when it is about to degrade • Do something about it! • What to do: ask for/contact new peers, contact server farm, etc. • Abstract example