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Transport of Real-Time Flows

Transport of Real-Time Flows. Bernd Girod Information Systems Laboratory Stanford University. THE MEANING OF FREE SPEECH The acquisition by eBay of Skype is a helpful reminder to the world's trillion-dollar telecoms industry that all phone calls will eventually be free . . .

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Transport of Real-Time Flows

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  1. Transport of Real-Time Flows Bernd Girod Information Systems Laboratory Stanford University

  2. THE MEANING OF FREE SPEECH The acquisition by eBay of Skype is a helpful reminder to the world's trillion-dollar telecoms industry that all phone calls will eventually be free . . . . . . Ultimately—perhaps by 2010—voice may become a free internet application, with operators making money from related internet applications like IPTV . . . [Economist, September 2005]

  3. IPTV Rollout Verizon 10M householdsby 2009 IPTV SBC18M households by 2007 [IEEE Spectrum, Jan. 2005]

  4. Why Is Real-Time Transport Hard? Internet is a best-effort network . . . CongestionInsufficient rate to communicate Packet loss Impairs perceptual quality Delay Impairs interactivity of services; Telephony: one way delay < 150 ms [ITU-T Rec. G.114] Delay jitter Obstructs continuous media playout

  5. Heterogeneous Applications Econo-mics & Regu-lation NetworkArchitecture Security Heterogeneous Physical Layers Clean Slate Internet DesignResearch Areas

  6. Outline of the Talk • QoS vs. best effort • Resource allocation for IPTV • Rate-distortion optimized streaming

  7. How 1B Users Share the Internet Rate r TCP Throughput RTT maximum transfer unit data rate Growing congestion p packet loss rate 0.0001 0.001 0.01 0.1 round trip time [Mahdavi, Floyd, 1997] [Floyd, Handley, Padhye, Widmer, 2000]

  8. Reservation-ism Voice and video need guaranteed QoS (bandwidth, loss, delay) Implement admission control: “Busy tone” when network is full Best effort is fine for data applications Best Effort-ism Best Effort good enough for all applications Real-time applications can be made adaptive to cope with any level of service Overprovisioning always solves the problem, and it’s cheaper than QoS guarantees QoS vs. Best Effort

  9. Simple Model of A Shared Link • Link of capacity C is shared among k flows • Fair sharing: each flow uses data rate C/k • Homogeneous flows with same utility function u(.) • Total utility C [Breslau, Shenker, 1998]

  10. Rigid Applications u • Utility u=0 below of minimum bit-rate B • Maximum total utility U=k* is achieved by admitting at most k* flows 1 C/k B [Breslau, Shenker, 1998]

  11. Rigid Applications (cont.) • Expected loss in total utility w/o admission control • Gap DU is substantial when number of admissable flows k* is small • Gap DU usually disappears with growing capacity COverprovisioning can solve the problem! [Breslau, Shenker, 1998]

  12. Elastic Applications • Elastic applications: utility function u(k), such that total utility U(k)=ku(C/k) increases with k • Example: u(C/k)=1-aC/k • All flows should be admitted: best effort! u C/k

  13. H.264 video coding for 2 different testsequences Video is elastic application Rate must be adapted to network throughput How to achieve rate control for stored content or multicasting? Utility function depends on content: should use unequal rate allocation Video Compression Good picture quality Foreman Mobile Bad picture quality

  14. Different Utility Functions • Example: uk(rk)=1-akrk • With rk>=0  Karush-Kuhn-Tucker conditions (“reverse water-filling”) • Better than utility-oblivious “fair” sharing Equal-slope “Pareto condition” uk Vilfredo Pareto 1848-1923 rk

  15. Distribution of IPTV over WLAN 5 Mbps 11 Mbps 2 Mbps Home Media Gateway [courtesy: van Beek, 2004]

  16. Video Streaming Over Shared Channel Transcoder Decoder 0 0 Transcoder Decoder 1 1 Receiver Transcoder Decoder 2 (Multi-Channel) 2 Transcoder Decoder 3 3 Controller [Kalman, van Beek, Girod 2005]

  17. Dynamically Changing RD Function Scene Cut Weighted average of last I-Frame, P-Frame and B-Frame used for RD estimation

  18. Average PSNR: 37.6 dB Average PSNR Stream 4: 35.5 dB Utility-Based Resource Allocation Equal Time Allocation Min-MSE Allocation • Average PSNR: 37.5 dB • Average PSNR Stream 4: 32.9 dB

  19. Tx Backlog for 4 Video Streams 85% WLAN Utilization [Kalman, van Beek, Girod 2005]

  20. Streaming of Stored Content Media files are already compressed: How can we nevertheless adapt to network? 100s to 1000ssimultaneous streams DSL Cable wireless Network Server Client

  21. Not All Packets are Equally Important I I I B P B P B P I B P B P B P … … … A A …

  22. Not All Packets are Equally Important I I I B P B P B P I B P B P B P … … … A A …

  23. Distortion-Aware Packet Dropping Good Picture quality Distortionaware Packet dropping No retransmissions QCIF Carphone I-P-P-P-P-P- . . . Bad picture quality Oblivious Percentage of Packets Retained [%] [Chakareski, Girod, ICME 2004]

  24. Repeat request Repeat request Repeat request Rate-distortion preamble Video data Request stream Packetschedule Rate-DistortionOptimized (RaDiO) Streaming “Decide which packets to send (and when) to maximize picture quality while not exceeding an average rate” [2001] Server Network Client

  25. A Brief History of Media Streaming • Media streaming w/o congestion avoidance: “reckless driving” [1995] • TCP-friendly rate control: “Limit average rate for fair sharing with TCP” [1997] • Rate-distortion optimized packet scheduling (RaDiO): “Decide which packets to send (and when) to maximize picture quality while not exceeding an average rate” [2001] • Congestion-distortion-optimized scheduling/routing (CoDiO): “Decide which packets to send (and when) to maximize picture quality while minimizing network congestion.” [2004]

  26. Congestion D [seconds] Rate R Congestion vs. Rate • Congestion: queuing delay that packets experience • weighted by size of the packet • averaged over all packets in the network • Congestion increases nonlinearly with link bit-rate Rmax

  27. Self congestioncauses late loss Video Distortion with SelfCongestion Good Picture quality Bad picture quality Bit-Rate [kbps]

  28. Streaming with Last Hop Bottleneck Random cross traffic High bandwidth links Video traffic Low bandwidth last hop Acknowledgments

  29. Delay distribution • Overall delay distribution • Queue length determines delay of last hop pdf delay C

  30. Comparison RaDiO vs. CoDiO 50 % PSNR [dB] PSNR [dB] Rate [kbps] End-to-end delay [ms] Simulations using H.263+ Rate : 10 fps Sequence : Foreman (32kbps,32kbps) Sequence length : 60s Playout deadline : 600ms

  31. Concluding Remarks • Over-provisioning makes QoS superfluous • Elastic applications don’t need QoS • Joint rate control for access bottlenecks (e.g. IPTV, WLAN) • Media-aware congestion control (e.g. CoDiO)

  32. The End http://www.stanford.edu/~bgirod/publications.html

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