Videos are expected to be a primary contributor to an anticipated massive surge in mobile network data. Caching the videos within the mobile network can significantly reduce the network load and Operational Expenditure (OPEX) for mobile network operators. Multi-access Edge Computing (MEC) can enable the video caching by providing processing and storage capabilities within the network. However, content providers usually employ Dynamic Adaptive Streaming over HTTP (DASH) for video streaming, which contains multiple bit-rate representations of videos. Constrained by its capacity, MEC can not cache all representations of popular videos. Video transcoding mitigates this issue to a certain extent by converting the higher available video bit-rate to a requested lower one; but, it can quickly exhaust the available edge processing power by transcoding a large number of videos in parallel. Therefore, caching appropriate video bit-rates that can serve the maximum number of users in the network is a non-trivial problem. To resolve this problem and to efficiently utilize the resources (processing and storage) at the network edge, we took a non-traditional approach for video caching that utilizes the network information provided by MEC's Radio Network Information (RNI) Application Program Interface (API). In particular, RNI API provides Radio Access Network (RAN) status information that can be employed to estimate the probability distribution of requested video qualities. In this work, we formulate the video caching problem as an Integer Linear Programming (ILP) for the hit-rate maximization. Since the optimization problem requires the knowledge of all future requests, it obviously cannot be used in real-time. Therefore, we develop a RAN-aware Adaptive VidEo cachiNg (RAVEN) method that uses network information to make an informed decision for video bit-rate selection in video caching coupled with transcoding and maximizes the number of served users form the network edge. Simulation results demonstrate that the RAVEN significantly outperforms state-of-the-art algorithms in the domain and performs closer to the optimal solution.

预计视频将成为预计移动网络数据激增的主要推动力。在移动网络中缓存视频可以显着减少移动网络运营商的网络负载和运营支出（OPEX）。通过在网络内提供处理和存储功能，多路访问边缘计算（MEC）可以启用视频缓存。但是，内容提供商通常使用HTTP动态自适应流（DASH）进行视频流传输，其中包含视频的多种比特率表示。受其容量限制，MEC无法缓存流行视频的所有表示。视频转码通过将较高的可用视频比特率转换为请求的较低比特率在一定程度上减轻了此问题；但，它可以通过并行转换大量视频来快速耗尽可用的边缘处理能力。因此，缓存适当的视频比特率以服务网络中最大数量的用户是一个不小的问题。为了解决此问题并有效利用网络边缘的资源（处理和存储），我们采用了一种非传统的视频缓存方法，该方法利用了MEC的无线网络信息（RNI）应用程序接口（API）提供的网络信息。 。特别是，RNI API提供了可用于估计请求的视频质量的概率分布的无线接入网（RAN）状态信息。在这项工作中，我们将视频缓存问题公式化为达到命中率最大化的整数线性规划（ILP）。由于优化问题需要了解所有将来的要求，因此显然不能实时使用。因此，我们开发了一种RAN感知的自适应VidEo cachiNg（RAVEN）方法，该方法使用网络信息为结合了转码的视频缓存中的视频比特率选择做出明智的决定，并最大程度地增加网络边缘用户的数量。仿真结果表明，RAVEN的性能明显优于领域中的最新算法，并且性能更接近最佳解决方案。我们开发了一种RAN感知的自适应VidEo cachiNg（RAVEN）方法，该方法使用网络信息为视频缓存中的视频比特率选择做出明智的决策，并进行转码，并最大程度地利用网络边缘来服务用户。仿真结果表明，RAVEN的性能明显优于领域中的最新算法，并且性能更接近最佳解决方案。我们开发了一种RAN感知的自适应VidEo cachiNg（RAVEN）方法，该方法使用网络信息为视频缓存中的视频比特率选择做出明智的决策，并进行转码，并最大程度地利用网络边缘来服务用户。仿真结果表明，RAVEN的性能明显优于领域中的最新算法，并且性能更接近最佳解决方案。