An Efficient Frame Work for Traffic Analysis in the Clouds

Abstract

While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming such has mobile networks, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: AMoV(adaptive mobile video streaming) andESoV(efficientsocialvideosharing).AMoVandESoVconstructaprivateagenttoprovidevideostreamingservicesefficientlyforeachmobileuser.Foragivenuser,AMoVletsherprivateagentadaptivelyadjustherstreamingflowwithascalablevideocodingtechniquebasedonthefeedbackoflinkquality.Likewise,ESoVmonitorsthesocialnetworkinteractionsamongmobileusers,andtheirprivateagentstrytoprefetchvideocontentinadvance.WeimplementaprototypeoftheAMES-Cloudframeworktodemonstrateitsperformance.Itisshownthattheprivate agentsinthecloudscaneffectivelyprovidetheadaptivestreaming,andperformvideosharing(i.e.,prefetching)based on the social network analysis.