The increasing consumption of video streams and the demand for higher-quality content drive the evolution of telecommunication networks and the development of new network accelerators to boost media delivery while optimizing network usage. Multi-access Edge Computing (MEC) enables the possibility to enforce media delivery by deploying caching instances at the network edge, close to the Radio Access Network (RAN). Thus, the content can be prefetched and served from the MEC host, reducing network traffic and increasing the Quality of Service (QoS) and the Quality of Experience (QoE). This paper proposes a novel mechanism to prefetch Dynamic Adaptive Streaming over HTTP (DASH) streams at the MEC, employing a Machine Learning (ML) classification model to select the media segments to prefetch. The model is trained with media session metrics to improve the forecasts with application layer information. The proposal is tested with Mobile Network Operators (MNOs)' 5G MEC and RAN and compared with other strategies by assessing cache and player's performance metrics.

翻译：随着视频流消费量持续增长及对更高质量内容的需求日益提升，通信网络加速演进，新型网络加速器应运而生，旨在优化网络资源利用的同时提升媒体传输效率。多接入边缘计算（MEC）通过在靠近无线接入网（RAN）的网络边缘部署缓存实例，为强化媒体传输提供可能。由此，可在MEC主机上预取并分发内容，从而降低网络负载，提升服务质量（QoS）与体验质量（QoE）。本文提出一种新型机制，在MEC中预取基于HTTP的动态自适应流（DASH）媒体数据，采用机器学习（ML）分类模型选择待预取的媒体片段。该模型利用媒体会话度量进行训练，通过引入应用层信息提升预测精度。本方案在移动网络运营商（MNO）的5G MEC与RAN环境中完成测试，并通过缓存性能与播放器性能指标与其他策略进行对比评估。