  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.
