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Mobile Networks and Management. 13th EAI International Conference, MONAMI 2023, Yingtan, China, October 27-29, 2023, Proceedings

Research Article

Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-55471-1_19,
        author={Qiuming Liu and Hao Chen and Yang Zhou and Dong Wu and Zihui Li and Yaxin Bai},
        title={Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System},
        proceedings={Mobile Networks and Management. 13th EAI International Conference, MONAMI 2023, Yingtan, China, October 27-29, 2023, Proceedings},
        proceedings_a={MONAMI},
        year={2024},
        month={3},
        keywords={VR video streaming Content prediction Caching algorithm Quality of experience Edge computing},
        doi={10.1007/978-3-031-55471-1_19}
    }
    
  • Qiuming Liu
    Hao Chen
    Yang Zhou
    Dong Wu
    Zihui Li
    Yaxin Bai
    Year: 2024
    Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System
    MONAMI
    Springer
    DOI: 10.1007/978-3-031-55471-1_19
Qiuming Liu1,*, Hao Chen1, Yang Zhou2, Dong Wu2, Zihui Li1, Yaxin Bai1
  • 1: School of Software Engineering, Jiangxi University of Science and Technology
  • 2: Information and Communication Branch, State Grid Jiangxi Electric Power Co.
*Contact email: liuqiuming@jxust.edu.cn

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.

Keywords
VR video streaming Content prediction Caching algorithm Quality of experience Edge computing
Published
2024-03-17
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-55471-1_19
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