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Rate Distortion Optimization for Mesh-based P2P Video Streaming

Rate Distortion Optimization for Mesh-based P2P Video Streaming Tareq Hossain, Yi Cui, Yuan Xue V anderbilt A dvanced Net work and S ystems Group Vanderbilt University, USA Presenter: Dr. Sachin Agarwal Deutsche Telekom Laboratories Outline Motivation Video Broadcast Can P2P Help?

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Rate Distortion Optimization for Mesh-based P2P Video Streaming

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  1. Rate Distortion Optimization for Mesh-based P2P Video Streaming Tareq Hossain, Yi Cui, Yuan Xue Vanderbilt Advanced Network and Systems Group Vanderbilt University, USA Presenter: Dr. Sachin Agarwal Deutsche Telekom Laboratories

  2. Outline • Motivation • Video Broadcast • Can P2P Help? • Rate Distortion for P2P Mesh • Rate Optimization • Simulation • Results • Conclusion

  3. Motivation • Video Broadcast • Increasing popularity due to wide use of internet • Can P2P Help? • Cost effective resource utilization • CPU cycles • Storage space • Uplink bandwidth • Instant deployability • Almost ubiquitous network coverage in the absence of CDN services and IP multicast

  4. Rate Distortion for P2P Mesh • Mesh based P2P can fully utilize the network resources of its peers compared to a tree based network • We use distributed algorithm – each peer adjusts its own streaming rate to reach the global optimum by satisfying: • Capacity constraint • Relay constraint • Double Pricing Solution • Simultaneous incorporation of capacity constraint and relay constraint significantly reduces the aggregate rate distortion • Single Pricing Solution • Relay constraint is applied after rate distortion algorithm converges • We present rate-distortion optimization for P2P mesh network • Double pricing solution performs better than single pricing solution

  5. Outline • Motivation • Rate Optimization • Performance Evaluation • Problem Formulation • Distributed Algorithm • Simulation • Results • Conclusion

  6. Performance Evaluation • Video quality is measured as the Mean-Square-Error (MSE) averaged over all frames • PSNR is used to quantify video quality, defined by • D represents the overall Mean-Square-Error (MSE) averaged over all frames of an encoded video sequence • The distortion D as a function of streaming rate xf is given by • The variables (θ, x0 and D0 ) depend on encoded video sequence as well as on the percentage of intra coded macroblocks.

  7. Problem Formulation Rate optimization is a convex function of the allocated rate Here f represents a flow between two peers, x is the rate vector and c is the capacity vector A is an L x F (link, flow) matrix of links and flows such that Alf = 1 if flow f goes through link l and 0 otherwise B is an F x F sparse matrix, where ((hk – 1)H + hi)th row is active only if there is a flow from peer hk to peer hi. Formally,

  8. Distributed Algorithm • Each receiving peer ( ) calculates the rates of its incoming flows in a mesh • Network price: • Net relay price: • Source Rate update for each peer: • Rate is updated based on the minimum of network and net relay price available among the all incoming flows • Rate update for incoming flows: • Rate update for incoming flows with minimum network and net relay price: link price relay price

  9. Outline • Motivation • Rate Optimization • Simulation • Configuration • Input Data • Multicast Tree Construction • Results • Conclusion

  10. Configuration • To determine the actual allocated rate, we choose the highest quantized rate that is immediately less than the rate achieved by our solution • The ITU-T test sequences used are: foreman, akiyo, hall, mother-daughter • The server has a fixed rate of 2Mbps • The maximum number of peers ~160 • The uplink bandwidth of each peer is randomly assigned between 0.6Mbps and 2Mbps

  11. Input data • The PSNR-Rate video input data (a) and Number of peers-Time data (b): Rate (Kbps)

  12. Multicast Mesh Construction • Peers join the streaming network one-by-one • Joining peer uses the spare capacity of existing peers to determine a suitable parent. The spare coefficient is defined as • Here xf(h)is the incoming flow rate of the peer h • Implementation • At the end of each rate update cycle, peers send their spare coefficient value to parents • The ID of the best suitable parent propagates to the server

  13. Outline Motivation Rate Optimization Simulation Results Conclusion

  14. Results • The average PSNR gain over all the videos for the double pricing solution is 1.86 dB (PSNR is 0 when all peers leave ~720s)

  15. Results • The average gain for the double pricing solution represented in terms of rate

  16. Conclusion We present an optimal rate allocation solution for P2P mesh network We use non-linear optimization framework Minimize aggregate distortion Maximize the overall PSNR among all peers in a P2P mesh Simultaneously apply peer relaying constraint along with capacity constraint Double pricing solution consistently performs better than single pricing solution

  17. Thank You VANETS (Vanderbilt Advanced Network and Systems) Group http://vanets.vuse.vanderbilt.edu QUESTIONS?

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