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Content-tracked Rate Control for Multiple Video Steaming Over Shared IEEE 802.11 Channel

Content-tracked Rate Control for Multiple Video Steaming Over Shared IEEE 802.11 Channel. Hongjiang Xiao hjxiao@stanford.edu. Outline. Overview Motivation Disadvantages analysis of current schemes Goals. Video Delivery over Wireless Internet. Many applications Mobile Video Phone

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Content-tracked Rate Control for Multiple Video Steaming Over Shared IEEE 802.11 Channel

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  1. Content-tracked Rate Control for Multiple Video Steaming Over Shared IEEE 802.11 Channel Hongjiang Xiao hjxiao@stanford.edu H. Xiao, EE 398B Project Proposal

  2. Outline • Overview • Motivation • Disadvantages analysis of current schemes • Goals H. Xiao, EE 398B Project Proposal

  3. Video Delivery over Wireless Internet • Many applications • Mobile Video Phone • Live TV broadcast • Surveillance • … • Unicast, multicast, broadcast H. Xiao, EE 398B Project Proposal

  4. Challenges and Techniques • Challenges • High bit error rate • Limited and time-varying bandwidth • Interference between neighboring nodes • Resource constraints (Battery, CPU, etc.) • … • Techniques • Adaptive error protection • Adaptive congestion control • Joint source-channel coding (JSCC) • Cross-layer design • … • Core Ideas: • Trade off between distortion, Rate, complexity • Adaptation to the Network conditions H. Xiao, EE 398B Project Proposal

  5. Outline • Overview • Motivation • Disadvantages analysis of current schemes • Goals H. Xiao, EE 398B Project Proposal

  6. Motivation • Past research works mainly • Address end-to-end QoS issue of single video sender-receiver pair • Do not care about other potential video steams • Regards all the other flows as background traffic • Real network scenario • Multiple video streams • All the streams share and compete for the physical resource Real network scenario H. Xiao, EE 398B Project Proposal

  7. Why Rate Control for Multiple Video Streams? • IEEE 802.11 MAC • Random access scheme: CSMA/CA protocol • Contention will occur among simultaneously transmitted packets • Only one packet can finally succeed in a transmission slot after backing off • What will happen without RC? • Network throughput drop greatly • Congestion increases • Video quality decreases Distributed Rate Allocation Protocol [X. Zhu, B. Griod ‘05 ‘07] H. Xiao, EE 398B Project Proposal

  8. Outline • Overview • Motivation • Disadvantages analysis of current schemes • Goals H. Xiao, EE 398B Project Proposal

  9. Source Distortion Model • Two most cited models • Trial encodings and nonlinear regression fitting • Based on -domain analysis • is the percentage of zeros among the quantized DCT coefficients. • Slope is predicted from the statistics of previous video frame or MBs K. Stuhlmller, B. Girod et al, ‘00 Z. He, S.K. Mitra, ‘01 H. Xiao, EE 398B Project Proposal

  10. Why Online R-D estimation? • Drawbacks of Most models for Live application • Parameters are estimated by offline training • Less ability to track the changing R-D characteristics induced by scene alteration • Accuracy of rate allocation algorithm decrease • Possible Approach: content track • Learn from past R-D performance of recently compressed frames • Statisticsof incoming uncompressed video signal X. Zhu, Proposal Suggestion H. Xiao, EE 398B Project Proposal

  11. Incorporate the Transmission Distortion? • Convex Optimized Framework for Rate allocation in reference paper[2] • Only considers encoding distortion • Try to consider the contribution of transmission distortion to the total distortion • Reflecting the impact of packet loss • Congestion, queue buffer overflow • MAC contention • Wireless channel error H. Xiao, EE 398B Project Proposal

  12. Outline • Overview • Motivation • Disadvantages analysis of current schemes • Goals H. Xiao, EE 398B Project Proposal

  13. Goals • Develop a Content-tracked Rate ControlScheme for maximizing the overall quality for multiple simultaneous video streams • Construct Online R-D estimation algorithm (1~1.5 weeks) • track the change of the video contents • Investigate the relationship between rate and end-to-end delay under the CSMA/CA mechanism (0.5~1 week) • Design Rate-Distortion-Delay optimized rate control algorithm (2 weeks) • Tradeoff of RDO and CDO • Incorporate the expected transmission distortion • Compare it with the traditional TCP-friendly Rate Control (TFRC) method H. Xiao, EE 398B Project Proposal

  14. References • [1] Q. Zhang, W. Zhu, and Y. Zhang, “End-to-End QoS for Video Delivery Over Wireless Internet,” Proceedings of the IEEE, Vol. 93, No. 1, January 2005 • [2] X. Zhu, E. Setton and B. Girod, “Content-Adaptive Coding and Delay-Aware Rate Control for A Multi-Camera Wireless Surveillance Network,” Proc. Multimedia Signal Processing (MMSP-05) • [3] X. Zhu and B. Girod, “Distributed Rate Allocation for Video Streaming over Wireless Networks with Heterogeneous Link Speeds,” The 3rd International Symposium on Multimedia over Wireless (ISMW’07) , Submitted. • [4] K. Stuhlmller, N. Farber, M. Link, and B. Girod, “Analysis of video transmission over lossy channels,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 6, pp. 1012.32, June 2000 • [5] Z. He, S.K. Mitra, “A Unified Rate-Distortion Analysis Framework for Transform Coding,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 11, No. 12, Dec. 2001. • [6] NS-2, http://www.isi.edu/nsnam/ns/ H. Xiao, EE 398B Project Proposal

  15. Thank you! H. Xiao, EE 398B Project Proposal

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