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Multi-layer Active Queue Management and Congestion Control for Scalable Video Streaming

Multi-layer Active Queue Management and Congestion Control for Scalable Video Streaming. Kang, S.-R.; Zhang, Y.; Dai, M.; Loguinov, D.; Distributed Computing Systems, 2004. Proceedings. 24th International Conference on , 24-26 March 2004 . Core components. Priority Active Queue Management

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Multi-layer Active Queue Management and Congestion Control for Scalable Video Streaming

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  1. Multi-layer Active Queue Management and Congestion Control for Scalable Video Streaming Kang, S.-R.; Zhang, Y.; Dai, M.; Loguinov, D.;Distributed Computing Systems, 2004. Proceedings. 24th International Conference on , 24-26 March 2004

  2. Core components • Priority Active Queue Management • mark packets of different importance and drop less important packets first • Congestion Control • feedback network information from router and adjust the frame size • Partitioned Enhancement Layer Streaming (PELS) • priority marking, AQM, congestion control

  3. Outline • Background • Best-effort network is not enough • AQM • Congestion control • Simulation • Conclusion

  4. Goal • majority of packets across bottleneck carry useful information • retransmission-free

  5. MPEG-4 FGS • base layer is more important than enhancement layer

  6. Best-Effort Streaming • assume independent Bernoulli packet loss with probability p, expected number of useful packets (consecutively received) is

  7. Best-Effort Streaming

  8. Optimal Preferential Streaming • goal: achieve U = 1 • in order to be optimal, upper layer should be dropped before lower layer • enhancement layers further divide into two layers

  9. Active Queue Management • two types of queues: PELS queue and Internet queue • Weighted round-robin (WRR)

  10. Active Queue Management • send low-priority packets only after all high-priority packets are sent • no end-user can gain by marking all packets with highest priority

  11. Selection of γ • pR = pxi/γxi = p/γ= pthr • optimistic: pthr~1  U~1 • pessimistic: pthr~p  γ =1  yellow layer = (1- γ)xi = 0 • close-form expression for γ

  12. Selection of γ • when p=0.1, pthr=0.75, U>=0.96 • when p=0.01, pthr=0.75, U>=0.996

  13. Congestion control • modified from Kelly’s control (a game-theoretic and optimization method), discrete version called Max-min Kelly Control (MKC) • reduce bitrate and keep waste to minimum

  14. Simulation • ns2 • simple bar-bell topology • 1 video frame = 63000 bytes = 126 packets(21 base layer packets) • 50% bottleneck forTCP cross traffic

  15. Stability properties ofγ • show that • γis stable in the close-form expression with dynamic loss prob; • by using found γ , loss prob of red packets kept to target threshold

  16. Delay characteristic of PELS

  17. Fairness of MKC congestion control

  18. Conclusion • preferential streaming framework (PELS) provides high level of end-user QoS • independent of underneath congestion control methods

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