Please use this identifier to cite or link to this item: https://doi.org/10.1145/2557642.2557647
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dc.titleGTube: Geo-predictive video streaming over HTTP in mobile environments
dc.contributor.authorHao, J.
dc.contributor.authorZimmermann, R.
dc.contributor.authorMa, H.
dc.date.accessioned2014-07-04T03:13:10Z
dc.date.available2014-07-04T03:13:10Z
dc.date.issued2014
dc.identifier.citationHao, J.,Zimmermann, R.,Ma, H. (2014). GTube: Geo-predictive video streaming over HTTP in mobile environments. Proceedings of the 5th ACM Multimedia Systems Conference, MMSys 2014 : 259-270. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/2557642.2557647" target="_blank">https://doi.org/10.1145/2557642.2557647</a>
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78165
dc.description.abstractMobile video streaming sometimes suffers from playback interruptions which are typically due to considerable network bandwidth variations that a user may experience when s/he travels along a route. Segmented adaptive HTTP streaming that switches between video streams encoded at different bitrates - and hence different quality levels - can be used to alleviate the issues of variable bandwidth. An important issue with respect to users' perceived experience is how the system schedules the quality levels to match the endto-end network bandwidth capacity. It is very beneficial if the application can estimate the future network conditions in advance, and therefore perform quality control and buffer control wisely. This work describes GTube, a video streaming system for receivers equipped with a GPS positioning sensor in mobile environments. The available network bandwidth for each location is collected by mobile users and then this data along with the measured locations are uploaded and recorded in a server. Mobile devices that stream video can send queries to the server in order to better predict the near-future bandwidth availability and plan quality adaptations accordingly. Copyright 2014 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/2557642.2557647
dc.sourceScopus
dc.subjectDASH
dc.subjectGeo-prediction
dc.subjectLocation services
dc.subjectMobile video
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/2557642.2557647
dc.description.sourcetitleProceedings of the 5th ACM Multimedia Systems Conference, MMSys 2014
dc.description.page259-270
dc.identifier.isiutNOT_IN_WOS
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