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dc.contributor.advisorBovik, Alan C. (Alan Conrad), 1958-
dc.creatorBampis, Christos George
dc.date.accessioned2017-02-08T15:15:37Z
dc.date.available2017-02-08T15:15:37Z
dc.date.issued2016-12
dc.date.submittedDecember 2016
dc.identifierdoi:10.15781/T2WS8HR5K
dc.identifier.urihttp://hdl.handle.net/2152/45591
dc.description.abstractThis work studies subjective video quality of experience (QoE) in video streaming applications. Streaming content providers such as YouTube are increasingly deploying HTTP adaptive streaming (HAS) strategies, where the video content is first divided into data chunks then encoded at different bitrates. Based on the estimated network conditions, a client can determine which bitrate will be used for the segment to be played next. By studying previous works on subjective video quality, we first demonstrate that most subjective studies and QoE datasets are not driven by practical network constraints and may not be appropriate for real-world video streaming applications. Next, we describe our research efforts towards bridging this gap by designing the LIVE-Netflix QoE dataset, which simulates realistic network conditions in a typical video streaming scenario, using long video sequences.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectVideo streaming
dc.subjectQuality of experience
dc.titleSubjective quality of experience in video streaming
dc.typeThesis
dc.date.updated2017-02-08T15:15:37Z
dc.description.departmentElectrical and Computer Engineering
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical and Computer engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering
dc.creator.orcid0000-0003-0570-048X
dc.type.materialtext


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