In recent network architectures, multi-MEC cooperative caching has been introduced to reduce the transmission latency of VR videos, in which MEC servers' computing and caching capability are utilized to optimize the transmission process. However, many solutions that use the computing capability of MEC servers ignore the additional arithmetic power consumed by the codec process, thus making them infeasible. Besides, the minimum cache unit is usually the entire VR video, which makes caching inefficient. To address these challenges, we split VR videos into tile files for caching based on the current popular network architecture and provide a reliable transmission mechanism and an effective caching strategy. Since the number of different tile files N is too large, the current cooperative caching algorithms do not cope with such large-scale input data. We further analyze the problem and propose an optimized k-shortest paths (OKSP) algorithm with an upper bound time complexity of O((K * M + N) * M * logN)), and suitable for shortest paths with restricted number of edges, where K is the total number of tiles that all M MEC servers can cache in the collaboration domain. And we prove the OKSP algorithm can compute the caching scheme with the lowest average latency in any case, which means the solution given is the exact solution. The simulation results show that the OKSP algorithm has excellent speed for solving large-scale data and consistently outperforms other caching algorithms in the experiments.

翻译：在当前网络架构中，多MEC协同缓存被引入以降低VR视频的传输时延，通过利用MEC服务器的计算与缓存能力优化传输过程。然而，许多利用MEC服务器计算能力的方案忽略了编解码过程消耗的额外算力，导致实际不可行。此外，最小缓存单元通常为整个VR视频，造成缓存效率低下。针对上述挑战，我们基于当前主流网络架构将VR视频切分为切片文件进行缓存，并提供可靠的传输机制与有效的缓存策略。由于不同切片文件数量N过大，现有协同缓存算法难以处理此类大规模输入数据。我们进一步分析问题，提出一种优化的K最短路径（OKSP）算法，其时间复杂度上界为O((K * M + N) * M * logN)，适用于边数受限的最短路径问题，其中K为所有M个MEC服务器在协作域内可缓存的切片总数。我们证明OKSP算法能在任何情况下计算出平均时延最低的缓存方案，即所得解为精确解。仿真结果表明，OKSP算法在求解大规模数据时具有优异速度，且在实验中始终优于其他缓存算法。