Edge Computing Empowered Video Content Delivery Strategy in the Internet of Vehicles

With the advancements of mobile communication and autonomous driving technologies, user demand for mobile multimedia services in the Internet of Vehicles (IoV) has been gradually increasing, which has put a great deal of strain on communication and computing. The high mobility of vehicles and the complexity of network scenarios present significant hurdles to the improvement of the quality of in-car entertainment services, even while streaming media transmission technology helps to reduce transmission latency. This paper investigates a video content distribution strategy that combines edge leaning with edge caching, given that video data makes up the great bulk of makes up the great bulk of currently available entertainment information. Specifically, an adaptive bitrate video distribution technique is proposed for the multi-node video stream data transmission problem, which is carefully formulated as Markov decision process (MDP). Our suggested approach optimizes both the selection of video contents with varying compression bitrates and the allocation of vehicle-to-everything (V2X) communication sub-channels based on the communication environment, users’ locations and buffering states, and each network node’s cache information. The simulation results confirm that our suggested approach is beneficial in terms of cache hit ratio and quality of service (QoS).