cs2p: improving video bitrate selection and adaptation...
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Y. Sun, F. Lin, N. Wang @
X. Yin, J. Jiang, V. Sekar, B. Sinopoli @
T. Liu @
CS2P:ImprovingVideoBitrateSelectionand
AdaptationwithData-DrivenThroughputPrediction
BitrateadaptationiskeyforQoE
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• DASH=DynamicAdaptiveStreamingoverHTTP
• EntailnewQoE metrics,e.g.,lowbuffering,highvideoquality
• Needintelligentbitratecontrolandadaptation
Priorwork:Accuratethroughputpredictioncanhelp!
Accurate throughput predictionà Betterinitialbitrateselection
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� Fixedbitrate
� Adaptivebitrate
Accurate throughput predictionà Bettermidstreamadaptation
� ReplicatetheanalysisbyYinetal.atSIGCOMM2015[1]
p𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑𝑄𝑜𝐸 = /0123456789:6;:1<034=>?@ABC
4[1] X. Yin, et al. “A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP”. ACM SIGCOMM, 2015.[2] T. Y. Huang, et al. “A Buffer-Based Approach to Rate Adaptation: Evidence from a Large Video Streaming Service”. ACM SIGCOMM, 2014.
Openquestionsonpredictability!
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� Ourunderstandingofthroughputvariabilityandpredictabilityisquitelimited.
�Whattypesofpredictionalgorithmstouse?� Inthecontextofvideobitrateadaptation� Priorapproaches:30%+ ofpredictionswitherror≥0.2
Ourworkandcontributions
A large-scale analysis, providing data-driveninsights for predicting the throughput accurately.
Design of CS2P (Cross-Session Stateful Predictor):Improving bitrate selection and adaptation viathroughput modeling.
A practical implementation of CS2P and thedemonstration of improvements in video QoE.
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Outline
� Motivation
è Data-driven Observations
� CS2PApproach
� Evaluation
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� FromoperationalplatformofiQIYI.� iQIYI isaleadingonlinevideocontentproviderinChina.
� 20M+ sessions,8daysinSep.2015,� Eachsessionrecordsavg.throughputper6-secondepoch.
Datasetdescription
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Feature CoverageClientIP 3.2MClientISP 87ClientAS 161Province 33City 736Server 18
Observation1:Significantvariabilitywithinasession.
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50%ofsessionswithn-stddev ≥30%
20%ofsessionswithn-stddev ≥50%
Observation2:Stateful/persistentcharacteristics.
Anexamplesession.10
ThroughputvariationacrosstwoconsecutiveepochswithaparticularIP/16prefix.
Observation3:Similarsessionà Similarthroughput
ThroughputatdifferentsessionclusterswithparticularIP/8prefixes.11
Observation 4:Complexrelationshipbetweensessionfeaturethroughput
Combinationsofmultiplefeaturesoftenhaveamuchgreaterimpactthantheindividualfeature.12
Theimpactofthesamefeatureondifferentsessionscouldbevariable.
Outline
� Motivation
� Data-driven Observations
è CS2PApproach
� Evaluation
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Observation IdeaManysessionsexhibitstatefulcharacteristicsintheevolutionofthethroughput.
CS2Plearns Hidden-MarkovModels(HMM) tocapturethestatesandstatetransitions.
Sessionssharingsimilarcriticalcharacteristicstendtoexhibitsimilarthroughputpatterns.
CS2PgroupssimilarsessionssharingthesamecriticalfeaturevaluesandusesCross-Sessionpredictionmethodology.
Therelationshipbetweensessionfeaturesandthroughputarequitecomplex.
CS2Plearnsaseparate modelforeachsimilarsessionclustersinsteadofusingaglobal model.
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PredictionEngine
Step1:SessionClustering
Step2: ModelTraining
VideoServer
VideoPlayerClients
1.InitialThroughput2.PredictionModel Step3: Throughput
PredictionandBitrateSelection
ThroughputMeasurements
WorkflowofCS2P
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Sessionclustering-findingcriticalfeatures
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Allthesessionsfortraining
Agivensubsetofsessionfeatures
Sessionunderprediction
Sessionsmatchingselectedfeatureswith
Predictthethroughputofwiththesefilteredsessions
Tryanothersessionfeaturesubset
Repeattheseprocedurestofindthecriticalfeatureset,whichyieldsthemostaccuratethroughputpredictionof
ThroughputpredictionwithHMM
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ModelTrain:AHidden-MarkovModelperclusterviaEMalgorithm(offline).
State1Gaussian (0.43,0.052)
Mbps
State2Gaussian(2.41,1.492 )
Mbps
State3Gaussian(1.20,0.102 )
Mbps
0.972
0.876 0.970
0.0550.012
0.016
0.0200.069
0.010
Throughputpredictionandbitrateselection(online).
Throughput𝑊𝑡ϵ𝑹
HiddenState𝑋𝑡ϵ𝝌 Xt-1 Xt
Wt-1 Wt
Outline
� Motivation
� Data-driven Observations
� CS2PApproach
è Evaluation
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Trace-drivensimulationsetup
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iQIYI throughputtrace:• Non-overlappingtraces
oftrainingandtesting
Algorithmstocompare:1. History-basedpredictor:
• LastSample,Harmonic-Mean,AutoRegression
2. ML-basedpredictor:• SVR,GradientBoosting
Regressiontrees3. CFA[1]
Bitrateselectionmethod:
Videosource:• “Envivio”fromdash.jstest
website• EncodedinH.264/MPEG-4
in5bitratelevels• State-of-art:MPC[2]
[1] J. Jiang, et al. “CFA: A Practical Prediction System for Video QoE Optimization”. In Proc. of USENIX NSDI, 2016.[2] X. Yin, et al. “A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP”. In Proc. of ACM SIGCOMM, 2015.
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lMidstreamEpochpReducemedianerrorby50%
lMulti-epochAheadp9%predictionerrorfor10epochahead
p50%improvement
ThroughputPredictionAccuracyTakeaway
MidstreamThroughput
Reduceby50%
VideoQoE
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� 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑𝑄𝑜𝐸 = /0123456789:6;:1<034=>?@ABC
� QoE[1] isalinearcombinationofavg.videoquality,qualityvariation,totalrebuffer timeandstartupdelay.
[1] X. Yin, et al. “A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP”. In Proc. of ACM SIGCOMM, 2015.
Midstreamepoch
8%5%
19%
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Metrics vs.HM+MPC vs.BBAvg.Bitrate 10.9% 9.3%GoodRatio 2.5% 17.6%BitrateVariability -2.3% 5.6%Startup Delay 0.4% -3.0%OverallQoE 3.2% 14.0%
Takeaway:1. CS2PimprovesmostoftheQoE metrics,
exceptlongerstartupdelaythanBBandhigherbitratevariabilitythanHM.
2. TheoverallQoE improvementofCS2Pis3.2%toHMand14%toBB.
Pilotdeployment:multi-citytest
Conclusions
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l Goodpredictionè Betterbitrateselection&adaptationèImprovedvideoQoE
l KeyinsightsonthroughputvariabilitypEvolutionofintra-sessionthroughputexhibitsstatefulcharacteristics.
pSimilarsessionshavesimilarthroughputstructures.
l CS2P:Cross-sessionHMM-basedapproachlOutperformpriorpredictorsby50%inmidstreampredictionerror.lAchieve3.2%improvementtoHMand14%toBBinvideoQoE.