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Deep Learning Enabled Dynamic Reactive Video Caching in Mobile Edge Networks

The dynamic video streaming adaption and caching has been widely adopted in the proposals of future network, fulfilling the ever-increasing demand for seamless high-definition video transmission. Generally, the mobile video is transmitted in a two-stage wireless-wired hybrid link. There are totally different features in two stages, leading to a cask effect. Fortunately, the emergence of mobile edge computing (MEC) enables an intelligent interaction between these two stages. Moreover, the emergence of deep learning provides a new way to enhance the performance of such system, with higher flexibility and intelligence. Here, we propose an Deep learning enabled dynamic reactive video caching scheme deployed in MEC server. In this scheme, the caching server improves the utilization efficiency of the backhaul while MEC-based video quality adaption is used to adapt the rate to time-varying wireless channel. Deep Q-learning is also introduced to optimize the quality of experience (QoE) of the mobile video service in an intelligent way. Based on our simulation, the proposed scheme outperforms existing schemes with higher quality and stability in mobile video service.