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Online delay optimization for MEC and RIS-assisted wireless VR networks

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Abstract

As wireless networks continue to advance, virtual reality (VR) transmission over wireless connections is progressively transitioning from concept to practical application. Although this technology can significantly enhance the VR user experience, its development bottleneck lies in the computing capacity of devices and transmission latency. Considering the limited computational resources of VR devices for rendering tasks, multi-access edge computing (MEC) servers are introduced to provide powerful computing capabilities. To cope with transmission latency, reconfigurable intelligent surface (RIS) enhances links between base stations (BSs) and users. Based on these two technologies, we propose a RIS-assisted VR streaming model, where BSs are equipped with MEC servers to assist data rendering. Firstly, the user association, power control, and RIS phase shift optimization problems in the VR transmission system are jointly modeled and analyzed, establishing a long-term minimization of the interaction delay model. Secondly, by modeling the optimization problem as a Markov decision process (MDP), a joint optimization framework based on multi-agent deep reinforcement learning (MADRL) is proposed. In this framework, we have separately designed two dedicated algorithms for discrete and continuous variables. Furthermore, multiple agents can provide feedback based on user experience and cooperate with each other to improve the joint strategy. Finally, the performance and superiority of the proposed solution and algorithm are validated through simulation experiments in different application scenarios.

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Funding

This work was supported in part by the Major Research Plan of National Natural Science Foundation of China under Grant No. 92167103, in part by the National Natural Science Foundation of China under Grants No. 62172084, 62132004, 62032013, 61972079, in part by the Aeronautical Science Foundation of China under Grant No. 20230026050001, in part by the Young and Middle-Aged Leading Talents in Technological Innovation of Shenyang under Grant No. RC231173, in part by the Key Research and Development Program of LiaoNing under Grant No. 2023JH2/101300196, in part by the Fundamental Research Funds for the Central Universities under Grants No. N2324004-12, N2216009, N2216006, N2116004, and in part by the LiaoNing Revitalization Talents Program under Grant No. XLYC2007162.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Jie Jia, Leyou Yang, Jian Chen, Lidao Ma & Xingwei Wang

  2. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China

    Jie Jia & Jian Chen

  3. Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China

    Jie Jia & Jian Chen

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  1. Jie Jia
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Correspondence to Jie Jia.

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Jia, J., Yang, L., Chen, J. et al. Online delay optimization for MEC and RIS-assisted wireless VR networks. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03706-4

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