cs2p: improving video bitrate selection and adaptation...

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.