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.

中文翻译：

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移动边缘计算中车辆互联网内容缓存与资源分配联合优化方案

在高速自由流动的情况下，提出了一种基于车联网移动边缘计算的内容缓存和资源分配联合优化方案。车辆轨迹预测为实现车云协同缓存提供了基础。通过将请求车辆的业务数据预先缓存到边缘云网络和对接车辆，请求车辆可以同时通过V2V链接和V2I链接获取数据，从而减少了数据获取的延迟。因此，本文考虑了V2I和V2V链路的带宽以及边缘云缓存总数受限制的情况。然后，研究了带宽和缓存联合分配策略，以最小化数据采集的加权平均延迟。进一步开发了基于深度确定性策略梯度的边缘协作缓存算法。与Q学习和深度强化学习算法不同，该缓存算法可以很好地应用于可变连续带宽分配动作空间。此外，通过使用价值函数和策略函数的交互迭代，有效地提高了收敛速度。最后，通过对实际交通数据的分析，获得了车辆起步和停车路径的仿真结果。仿真结果表明，与其他几种较新的协作式缓存方案相比，该方案具有更好的性能。此外，通过使用价值函数和策略函数的交互迭代，有效地提高了收敛速度。最后，通过对实际交通数据的分析，获得了车辆起步和停车路径的仿真结果。仿真结果表明，与其他几种较新的协作式缓存方案相比，该方案具有更好的性能。此外，通过使用价值函数和策略函数的交互迭代，有效地提高了收敛速度。最后，通过对实际交通数据的分析，获得了车辆起步和停车路径的仿真结果。仿真结果表明，与其他几种较新的协作式缓存方案相比，该方案具有更好的性能。