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dc.contributor.authorParate, Piyush
dc.description.abstractVideo streaming on mobile devices such as PDA’s, laptop PCs, pocket PCs and cell phones is becoming increasingly popular. These mobile devices are typically constrained by their battery capacity, bandwidth, screen resolution and video decoding and rendering capability. Consequently, video personalization strategies are used to provide these resource-constrained mobile devices with personalized video content that is most relevant to the client’s request while simultaneously satisfying the client’s resource constraints. Since mobile clients are typically in remote network location from the personalizing servers, it is often desirable to cache intelligently portions of the video files at the caching proxies in order to reduce latency observed at the client end, and in the process also offload the data load on the server to local caching proxies. This research proposes design and implementation of video personalization server and multiple caching proxies, which can efficiently disseminate personalized videos to multiple resource-constrained clients. The video personalization servers use an automatic video segmentation and video indexing scheme based on semantic video content. The caching proxies implement a novel cache replacement and Multi-stage client request aggregation algorithm, specifically suited for caching personalized video files generated by the personalization servers. The cache design also implements a personalized video segment calculation algorithm based on client’s content preference and resource constraints. The video personalizing server and cache architecture is well suited for personalized video dissemination, with low client-experienced latency, to resource-constrained multimedia-enabled mobile devices such as mobile phones, PDAs and pocket PCs.
dc.subjectVideo Personalization, Caching, Cache Replacement, Request Aggregation
dc.titleTowards high performance personalized video content dissemination architecture for mobile devices
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorLakshmish Ramaswamy
dc.description.committeeLakshmish Ramaswamy
dc.description.committeeKang Li
dc.description.committeeSuchendra M. Bhandarkar

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