Federated Deep Reinforcement Learning-Based Caching and Bitrate Adaptation for VR Panoramic Video in Clustered MEC Networks

被引:4
|
作者
Li, Yan [1 ,2 ]
机构
[1] Jiangxi Univ Finance & Econ, Sch Informat Management, Nanchang 330032, Peoples R China
[2] Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Peoples R China
基金
中国国家自然科学基金;
关键词
federated deep reinforcement learning; VR panoramic video; caching and bitrate adaptation; hierarchical clustered MEC network;
D O I
10.3390/electronics11233968
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Virtual reality (VR) panoramic video is more expressive and experiential than traditional video. With the accelerated deployment of 5G networks, VR panoramic video has experienced explosive development. The large data volume and multi-viewport characteristics of VR panoramic videos make it more difficult to cache and transcode them in advance. Therefore, VR panoramic video services urgently need to provide powerful caching and computing power over the edge network. To address this problem, this paper establishes a hierarchical clustered mobile edge computing (MEC) network and develops a data perception-driven clustered-edge transmission model to meet the edge computing and caching capabilities required for VR panoramic video services. The joint optimization problem of caching and bitrate adaptation can be formulated as a Markov Decision Process (MDP). The federated deep reinforcement learning (FDRL) algorithm is proposed to solve the problem of caching and bitrate adaptation (called FDRL-CBA) for VR panoramic video services. The simulation results show that FDRL-CBA can outperform existing DRL-based methods in the same scenarios in terms of cache hit rate and quality of experience (QoE). In conclusion, this work developed a FDRL-CBA algorithm based on a data perception-driven clustered-edge transmission model, called Hierarchical Clustered MEC Networks. The proposed method can improve the performance of VR panoramic video services.
引用
收藏
页数:14
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