Abstract
Content prediction can avoid VR video streaming delay in mobile edge caching system. To reduce request delay, popular content should be cached on edge server. Existing work either focuses on content prediction or on caching algorithms. However, in the end-edge-cloud system, prediction and caching should be considered together. In this paper, we jointly optimize the four stages of prediction, caching, computing and transmission in mobile edge caching system, aimed to maximize the user’s quality of experience. We propose a progressive policy to optimize the four steps of VR video streaming. Since the user’s QoE is determined by the performance of the resource allocation and caching algorithm, we design a caching algorithm with unknown future request content, which can efficiently improve the content hit rate, as well as the durations for prediction, computing and transmission. We optimize the four stages under arbitrary resource allocation and simulate the proposed algorithm according to the degree of overlap, as well as completion rate. Finally, under the real scenario, the proposed algorithm is verified by comparing with several other caching algorithms, simulation results show that the user’s QoE is improved under the progressive policy and the proposed algorithm.
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Acknowledgment
This work was supported in part by Culture and Art Science Planning Project of Jiangxi Province (No. YG2018042), Humanities and Social Science Project of Jiangxi Province (No. JC18224).
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Liu, Q., Chen, H., Zhou, Y., Wu, D., Li, Z., Bai, Y. (2024). Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System. In: Wu, C., Chen, X., Feng, J., Wu, Z. (eds) Mobile Networks and Management. MONAMI 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-55471-1_19
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DOI: https://doi.org/10.1007/978-3-031-55471-1_19
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