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In a high-speed free-flow scenario, a joint optimization scheme for content caching and resource allocation is proposed based on mobile edge computing in Internet of Vehicles. Vehicle trajectory prediction provides the basis for the realization of vehicle-cloud collaborative cache. By pre-caching the business data of requesting vehicles to edge cloud networks and oncoming vehicles, requesting vehicles can obtain data through V2V link and V2I link at the same time, which reduces the data acquisition delay. Therefore, this paper considers the situation where bandwidth of V2I and V2V link and the total amount of edge cloud caches are limited. Then, the bandwidth and cache joint allocation strategy to minimize the weighted average delay of data acquisition is studied. An edge cooperative cache algorithm based on deep deterministic policy gradient is further developed. Different from Q-learning and deep reinforcement learning algorithms, the proposed cache algorithm can be well applied to variable continuous bandwidth allocation action space. Besides, it effectively improves the convergence speed by using interactive iteration of value function and strategy function. Finally, the simulation results of vehicle driving path at the start and stop are obtained by analyzing real traffic data. Simulation results show that the proposed scheme can achieve better performance than several other newer cooperative cache schemes.

A joint optimization scheme of content caching and resource allocation for internet of vehicles in mobile edge computing