bridging content-pipe divide
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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 - PowerPoint PPT PresentationTRANSCRIPT
Ph.D. Proposal
Bridging Content-Pipe DivideAmitabha GhoshHaris KremoJiasi ChenJosphat Magutt
April 28, 2011
##1AgendaContent-Pipe DivideContent-Aware NetworkingVideo Over WirelessImplementation (Theory vs. Practice)Quota-Aware Video Adaptation#Content-Pipe DivideContent SideMedia companies: own video and musicEnd-users: post video onlineOperators of CDN and P2P systemsSeek the best way to distribute contentThrough multimedia signal processing, caching, relaying, sharing, Treat network as just a means of transportationSeek the best way to manage network infrastructureThrough resource allocation on each link, between links, and end-to-endTreat content as just bits to transport between nodesPipe SideISPsEquipment vendorsNetwork management software vendorsMunicipalities and enterprisesDIVIDE#3Traditional ThinkingSeparation between content generation and transportation
TranscodeGenerate multimediaFramesShapingQueuingMarkingDroppingTransportation networkSeparation#
New ThinkingContent-Aware NetworkingAdjust PHY and MAC layer parameters to suitDrop packets by frame distortion (I, P vs. B)
Network-Aware Content GenerationSVC transcodingJoint summarization + resource allocationGOP: IPBBPBBPBB#Rate-Distortion FairTwo flows competing for BW over a common linkRate Fairness: Each flow gets half the capacityDistortion Fairness: Flow1 gets more capacity than Flow2
Flow1 with less motion helps Flow2 with rich motion#Distortion MetricPSNRCaptures only spatial variation
PCACaptures motion/activity
#Related WorksContent-Aware distortion-Fair dropping [Chiang 09]Minimize max end-to-end distortion in multi-hop wired networksUser-driven, threshold-based dropping based on frame priorities
Discrete time frame selection [Chiang 08]Voice + video, wireless, one-hop, multi-userJARS: 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 utilityBuffer modeling, value iteration, reinforcement learning, Bellmans equations, stochastic sub-gradient
#Related WorksModulation, MAC retry, path selection [van der Schaar 06]Cross-layer approach to maximize capacity-distortion utilityExhaustive search, greedy algorithm
Rate-distortion optimized streaming [Chou 06]Single user, wired networkScheduling policy vector to minimize expected distortion subject to rate constraint
Media-aware rate allocation [Girod 10]Proxy-server: receiver-driven, proxy-client: sender-drivenPolicy (Markov decision tree): which packets to select for transmissionIterative Sensitivity Analysis (ISA)
#
Problem FormulationCDMA 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 ControlCSMA vs. CDMA#ImplementationTheory vs. Practice#Closed loop power control for CSMAdriven by video qualityA software defined radio implementation study
Haris Kremo##OutlineImplementation
Power control algorithmtarget received power driven by video qualityrequires video profilingreceived signal strength (RSSI) feedback
Demo setup
Conclusionon theory vs. practice gap#Rice University WARP software defined radio PHY: 802.11 (p-like) OFDM64 carriers across 10 MHztransmit power adjustable in 0.5 dB stepsrange: -20 dBm to 10 dBmBPSK, QPSK, 16-QAM, 64-QAM
MAC: 802.11 DCFcarrier sensing through energy detectionexponential random backoffACK successful reception
programmableXilinx FPGA#Closed loop power controlSelect signal strength at receiver to match desired video qualityAdjust transmit power to achieve that signal strengthPSNR to RSSItargetPSNRreceiver
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DATAACKcalculate RSSI transmitter ireceiver jtime varying channel#Video profilingTabulate distortion vs. signal strength
Connect transmitter and receiver with a cableFor different fixed power levels in 2dBm steps:stream video and save it on the receiverrecord RSSIcalculate frame-by-frame distortion offline
original videodistortionfixed adjustable powerreceived video
RSSI #Experimental setupFour videos streaming to one receiverHigh Definition (HD) vs. Low Definition (LD)High Motion (HM) vs. Low Motion (LM)
Adjust manually target PSNR
HDHMHDLMLDHMLDLM
#17Theory vs. practiceCDMA vs. CSMAlicensed vs. unlicensed bandconnection based vs. packet based
Hard to calculate video metric in real time
RSSI not a good measure of interference
Practicalities inaccuracies: 1dB resolutionnonlinearities: set power out of rangeoutdated feedback:insufficient packet rate#Quota-Aware Video AdaptationJiasi Chen
April 28, 2011
#1919
System Architecture
End UserEdge ISPInternetContent Providercostdistortion of videos
VideoStores multiple precoded streams of each video#20MotivationWhats the best way to compress videos and stay within budget constraints, while maintaining perceptual quality?#Adaptation EngineAlgorithmInput videoClassifierOutput videoQuotaUser profileVideo profileProfiler
#22User Profiling
#23Optimization ProblemMaximize utilitySubject to budget constraintsSpecial case of knapsack problemOnline algorithm: video requests are not known in advanceAs each request arrives, make an on-the-fly decision of how much to compress#24Divide billing cycle into sessionsIn each session, create a knapsack based on predictionChoose 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)Online Algorithm
#25A possible wayOnline Algorithm
#26Bigger legend.
Consumer Cost SavingsDataCostFirst 200 MB$15Each additional 200 MB$15Quota = 200 MB#Rescale, distortion, show different users. Per gigabyte over27Thank you!#