Research On Content Cache Optimization Methods For Vehicle Videos

With the rapid development of 5G technology,it is expected that by 2022,the global mobile communication traffic will increase by about 8 times,of which video data traffic will account for 80%,and live video traffic will account for about 17%.In the Internet of Vehicles,edge caching brings video resources and data storage closer to the vehicle user than ever before.User requests for video are mainly divided into live video requests and on-demand video requests.Live video streams show outstanding interactivity,diversity and real-time characteristics,while on-demand video streams have the characteristics of large files and high repeated request rates.Therefore,it is necessary to formulate corresponding caching schemes according to the characteristics of different video requests.In this paper,according to the characteristics of live video stream and on-demand video stream,the video caching method in vehicle network is studied from the following two perspectives.Firstly,in view of the real-time and high resource requirements of live video streams in the vehicle network,this paper proposes a live video caching method,which solves the joint optimization problem of the server’s caching capacity and computing resources in the scenario of supporting edge caching and increases the average bitrate of live video streams.In this paper,the problem is modeled as an NP-hard integer linear programming problem and solved by a suboptimal algorithm with low time complexity,which is called an online iterative cache algorithm based on greedy algorithm.Secondly,in view of the characteristics of large files,high repeated request rate and limited edge node resources in the on-demand video stream in the vehicle network,this paper also considers the on-demand video cooperative cache in the edge cache cluster scenario to maximize the cache hit rate of the cluster.The placement problem is transformed into a multi-knapsack optimization problem.To solve this problem,a cache algorithm based on branch and bound method is designed.Finally,simulation experiments and verification analysis of the proposed algorithm are carried out.The experimental process corresponding to the model of maximizing the average video bit rate is to compare the proposed online iterative caching algorithm based on the greedy algorithm with the global optimal algorithm branch and bound method.It can have similar performance to the global optimal algorithm,and can better adapt to changes in the retention rate of live video streams over time.The experimental process corresponding to the model of maximizing the video cache hit rate is to compare the proposed branch-and-bound based cache algorithm with the greedy algorithm and the random algorithm,which proves that the algorithm proposed in this paper can effectively improve the cache hit rate.