Machine Learning-based Edge Caching for Video Streaming in 5G Networks

The advent of 5G networks has brought significant advancements in the Quality of Service (QoS) provided to various applications, including video streaming. However, the increasing demand for high-quality video streaming, coupled with the need for low latency and improved user experience, poses challenges for the existing network architecture. Recently, there have been several proposals to utilize machine learning techniques in order to improve the QoS for mobile video users. These techniques aim to enhance various aspects of video delivery, such as video streaming, video compression, and video adaptation. This paper aims to explore the use of edge caching machine learning-based technique for video streaming services. In this paper, proof-of-concept experiments and the setup of a Hybrid Cloud-Edge Architecture with Amazon Web Services are presented. The experimental results demonstrate that applying machine learning to cloud-edge caching architecture is both feasible and effective.