Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

翻译：流媒体内容缓存是推动未来城市车载网络价值链的关键使能技术。然而，车辆的高移动性、信息传输的间歇性、用户请求的高动态性、缓存容量的局限性以及业务场景的极端复杂性，给车载网络中的内容缓存与分发带来了巨大挑战。为解决此问题，本文旨在设计一种新型边缘计算赋能的层次化协作缓存框架。首先，我们深入分析了用户请求的历史车辆轨迹与时间-空间相关性，构建了用于预测车辆轨迹和内容流行度的系统模型，为移动感知的内容缓存与调度奠定基础。同时，我们探讨了隐私保护策略以实现隐私保护的预测模型。此外，基于轨迹和流行内容预测结果，研究了内容缓存策略，并针对层次化协作缓存网络提出了自适应动态资源管理方案。最后，通过仿真验证了所提方案与算法的优越性。结果表明，与传统方案相比，所提算法在命中率和平均时延方面有效提升了系统性能，并缩小了与最优缓存方案之间的差距。