Reinforcement Learning for Efficient and Low-Latency Video Content Delivery: Bridging Edge Computing and Adaptive Optimization

Abstract

In 2019, video transmission traffic made up 60.6% of overall Internet downlink traffic.  In the future, with the rapid development of 4K/8K, AR/VR, holographic communication, smart city, intelligent transportation, and other technologies, network video transmission demand and traffic will be further inspired.  In addition, the number of video users on the Internet has maintained a rapid growth tendency, not only due to the rapid improvement of traditional network bandwidth but also because the quick expansion of mobile Internet has further stimulated the potential of the video transmission market. This paper designs a video transmission optimization strategy that takes reinforcement learning and edge computing (TORE) to improve the video transmission efficiency and quality of experience.  Specifically, first, we design the popularity prediction model for video requests based on the RL (reinforcement learning) and introduce the adaptive video encoding method for optimizing the efficiency of computing resource distribution.  Second, we design a video caching strategy, which adopts EC (edge computing) to reduce the redundant video transmission.  Last, simulations are conducted, and the experimental results fully demonstrate the improvement of video quality and response time.