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1、1APoissonHiddenMarkovModelforMultiviewVideoTrafficLorenzoRossi,JacobChakareski,PascalFrossard,andStefaniaColonneseAbstract—Multiviewvideohasrecentlyemergedasameansresourceallocation,bufferdimensioning,andperformancetoimproveuserexperienceinnovelmultimediaservices.Weevaluation[8].proposeanews
2、tochasticmodeltocharacterizethetrafficThereishoweveralackoftrafficmodelsformultiviewgeneratedbyaMultiviewVideoCoding(MVC)variablebitvideocommunicationservices.Weproposehereanewtrafficratesource.Tothisaim,weresorttoaPoissonHiddenMarkovModel(P-HMM),inwhichthefirst(hidden)layerrepresentsthemodelfo
3、rMVCcontentinordertocharacterizetheframesizeevolutionofthevideoactivityandthesecondlayerrepresentsthesequenceobservedattheoutputofanMVCvariablebitrateframesizesofthemultipleencodedviews.Weproposeamethod(VBR)source.Specifically,buildingonourpreliminaryworkforestimatingthemodelparametersinlong
4、MVCsequences.[9],wedesignadoublystochasticsourcemodel,namelyaWethenpresentextensivenumericalsimulationsassessingthePoissonHiddenMarkovModel(P-HMM)[10],inwhichthemodel'sabilitytoproducetrafficwithrealisticcharacteristicsforageneralclassofMVCsequences.Wethenextendourfirst(hidden)layerconsistsof
5、anon-stationarychainmodelingframeworktonetworkapplicationswhereweshowthatourthevideoactivitylevelandthesecondlayerrepresentsthemodelisabletoaccuratelydescribethesenderandreceiverframesizesofthedifferentMVCencodedviews.Besides,webuffersbehaviorinMVCtransmission.Finally,wederiveaextendtheP-HM
6、Mparameterestimationalgorithmforshortmodelofuserbehaviorforinteractiveviewselection,which,inobservationsequencespresentedin[10]andadaptittolongconjunctionwithourtrafficmodel,isabletoaccuratelypredictactualnetworkloadininteractivemultiviewservices.sequencessuchasthoseencounteredinvideocommuni
7、cationservices.WeassessthemodelperformancesbyextensiveIndexTerms—Digitalvideobroadcasting,threedimensionalnumericalsimulationsonclassesofMVCsequencessharingTV,hiddenMarkovmodels,multiviewvideo.commonproperties.Weapplyourmodeltopredictthetrafficloadgenerat
