A Mobile Edge Caching Strategy for Video Grouping in Vehicular Networks

With the continuous boom in video services and advanced computing, the requirements of mobile users for network resource and performance are rising steadily. Mobile edge computing (MEC) technology has been applied in vehicular networks (VNs) in recent years to cope with high vehicle mobility and network topology change. In this paper, we propose a group-partitioned video caching strategy algorithm (GPC) in VNs. The algorithm first partitions the video requesters and then employs the Lagrange function and Lambert function to solve the cache probability matrix as optimization variable. Correspondingly, we choose caching hit ratio and latency as cache performance evaluation metrics we take the revenue function as optimization objective, and aim to maximize the revenue value. Experimental results show that that the dual influence of video file size and cache size is a significant factor in the probability of caching. Our GPC algorithm outperforms other existing algorithms in the revenue.