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Bridging Content-Pipe Divide

Bridging Content-Pipe Divide. Amitabha Ghosh Haris Kremo Jiasi Chen Josphat Magutt April 28, 2011. Agenda. Content-Pipe Divide Content-Aware Networking Video Over Wireless Implementation (Theory vs. Practice) Quota-Aware Video Adaptation. Content-Pipe Divide. Content Side

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Bridging Content-Pipe Divide

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  1. Bridging Content-Pipe Divide Amitabha Ghosh Haris Kremo Jiasi Chen Josphat Magutt April 28, 2011

  2. Agenda • Content-Pipe Divide • Content-Aware Networking • Video Over Wireless • Implementation (Theory vs. Practice) • Quota-Aware Video Adaptation

  3. Content-Pipe Divide • Content Side • Media companies: own video and music • End-users: post video online • Operators of CDN and P2P systems • Pipe Side • ISPs • Equipment vendors • Network management software vendors • Municipalities and enterprises D I V I D E • Seek the best way to distribute content • Through multimedia signal processing, caching, relaying, sharing, … • Treat network as just a means of transportation • Seek the best way to manage network infrastructure • Through resource allocation on each link, between links, and end-to-end • Treat content as just bits to transport between nodes

  4. Traditional Thinking • Separation between content generation and transportation Transportation network Shaping Queuing Marking Dropping Frames Generate multimedia Transcode Separation

  5. New Thinking • Content-Aware Networking • Adjust PHY and MAC layer parameters to suit • Drop packets by frame distortion (I, P vs. B) • Network-Aware Content Generation • SVC transcoding • Joint summarization + resource allocation GOP: IPBBPBBPBB

  6. Rate-Distortion Fair • Two flows competing for BW over a common link • Rate Fairness: Each flow gets half the capacity • Distortion Fairness: Flow1 gets more capacity than Flow2 Flow1 with less motion helps Flow2 with rich motion

  7. Distortion Metric • PSNR • Captures only spatial variation • PCA • Captures motion/activity

  8. Related Works • Content-Aware distortion-Fair dropping [Chiang ‘09] • Minimize max end-to-end distortion in multi-hop wired networks • User-driven, threshold-based dropping based on frame priorities • Discrete time frame selection [Chiang ‘08] • Voice + video, wireless, one-hop, multi-user • JARS: Joint Adaptation (summarization), Resource allocation (distributed pricing-based), Scheduling (greedy centralized TDM) • MU-MDP traffic state optimization [van der Schaar ‘10] • Maximize expected discounted accumulated utility • Buffer modeling, value iteration, reinforcement learning, Bellman’s equations, stochastic sub-gradient

  9. Related Works • Modulation, MAC retry, path selection [van der Schaar ‘06] • Cross-layer approach to maximize capacity-distortion utility • Exhaustive search, greedy algorithm • Rate-distortion optimized streaming [Chou ‘06] • Single user, wired network • Scheduling policy vector to minimize expected distortion subject to rate constraint • Media-aware rate allocation [Girod ‘10] • Proxy-server: receiver-driven, proxy-client: sender-driven • Policy (Markov decision tree): which packets to select for transmission • Iterative Sensitivity Analysis (ISA)

  10. Problem Formulation • CDMA Uplink: An Implementable Solution • : TX power of user i at time t • : SINR at BS from user i at time t • Rate: • Utility: negative distortion • Goal: subject to: SINR and deadline constraints • Scheduling vs. Power Control • CSMA vs. CDMA

  11. Implementation Theory vs. Practice

  12. Closed loop power control for CSMAdriven by video quality A software defined radio implementation study HarisKremo

  13. Outline • Implementation • Power control algorithm • target received power driven by video quality • requires video profiling • received signal strength (RSSI) feedback • Demo setup • Conclusion • on theory vs. practice gap

  14. Rice University WARP software defined radio • PHY: 802.11 (“p”-like) OFDM • 64 carriers across 10 MHz • transmit power adjustable in 0.5 dB steps • range: -20 dBm to 10 dBm • BPSK, QPSK, 16-QAM, 64-QAM • MAC: 802.11 DCF • carrier sensing through energy detection • exponential random backoff • ACK successful reception programmable Xilinx FPGA

  15. Closed loop power control • Select signal strength at receiver to match desired video quality • Adjust transmit power to achieve that signal strength target PSNR PSNR to RSSI receiver - time varying channel receiver j DATA ACK calculate RSSI transmitter i piggyback

  16. Video profiling • Tabulate distortion vs. signal strength • Connect transmitter and receiver with a cable • For different fixed power levels in 2dBm steps: • stream video and save it on the receiver • record RSSI • calculate frame-by-framedistortion offline original video received video fixed adjustable power RSSI distortion

  17. Experimental setup • Four videos streaming to one receiver • High Definition (HD) vs. Low Definition (LD) • High Motion (HM) vs. Low Motion (LM) • Adjust manually target PSNR HDLM HDHM LDHM LDLM

  18. Theoryvs. practice • CDMA vs. CSMA • licensed vs. unlicensed band • connection based vs. packet based • Hard to calculate video metric in real time • RSSI not a good measure of interference • Practicalities • inaccuracies: 1dB resolution • nonlinearities: set power out of range • outdated feedback: insufficient packet rate • …

  19. Quota-Aware Video Adaptation Jiasi Chen April 28, 2011

  20. System Architecture End User Edge ISP Internet Content Provider Video • Stores multiple precoded streams of each video distortion of videos cost

  21. Motivation What’s the best way to compress videos and stay within budget constraints, while maintaining perceptual quality?

  22. Adaptation Engine Profiler Quota User profile Algorithm Output video Input video Classifier Video profile

  23. User Profiling

  24. Optimization Problem • Maximize utility • Subject to budget constraints • Special case of knapsack problem • Online algorithm: video requests are not known in advance • As each request arrives, make an on-the-fly decision of how much to compress

  25. Online Algorithm • Divide billing cycle into sessions • In each session, create a knapsack based on prediction • Choose items for knapsack • Optimal to of offline algorithm(Chakrabarty et al., “Budget constrained bidding in keyboard auctions and online knapsack problems,” Proc. 17th Intl Conf WWW, 2008)

  26. Online Algorithm

  27. Consumer Cost Savings • Quota = 200 MB

  28. Thank you!

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