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• W3215728913 abstract "In this chapter, we investigate edge-assisted content caching and distribution for mobile virtual reality (VR) video streaming. In the considered VR use case, an edge server caches chunks of popular videos and delivers them to users. We focus on how a caching policy can improve the quality of experience of users while adapting to network dynamics given limited network resources. The research objective is to develop content placement and distribution schemes to cache high-quality and popular VR video chunks in a video-on-demand setting while reducing the video frame missing rate. To achieve this objective, we first propose a content placement scheme to make decisions on which video chunks to cache, while considering the trade-off among communication, computing, and caching resource utilization. Based on the content placement decisions, we further propose a learning-based content distribution scheme to allocate computing units given content delivery requests from multiple users. Specifically, a deep reinforcement learning approach is developed for the edge server to deliver content effectively and reduce the overall frame missing rate with low communication overhead. Simulation results are provided to verify the performance of proposed schemes." @default.
• W3215728913 created "2021-12-06" @default.
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• W3215728913 date "2021-01-01" @default.
• W3215728913 modified "2023-10-14" @default.
• W3215728913 title "Edge-Assisted Mobile VR" @default.
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• W3215728913 doi "https://doi.org/10.1007/978-3-030-88743-8_5" @default.
• W3215728913 hasPublicationYear "2021" @default.
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