Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Abstract

In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

LFDC: Low-Energy Federated Deep Reinforcement Learning for Caching Mechanism in Cloud–Edge Collaborative

“…Specifically, Rathoret et al [26] propose a proactive caching framework based on deep learning that achieves significant improvements in feedback link and QoE. Wan et al [27] employ an improved DRL method to optimize caching for enhancing video streaming quality of service. To mitigate the excessive network resource consumption of deep reinforcement learning during the transmission of training and testing data, Wang et al [13] propose a caching framework based on federated deep reinforcement learning.…”

Edge Caching and Computing for Wireless Networks

“…In addition to the above works, there are some rest papers in this special issue on the application of artificial intelligence on the wireless caching and computing networks, as shown in Refs. [4][5][6]. In particular, deep reinforcement learning was proposed in these works, in order to provide an intelligent solution to the system resource allocation, such as caching allocation and offloading allocation, bandwidth allocation, and power allocation.…”