Our TNSM 2022 journal article is the final publication funded by this project. The full title is “Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH)”. This is joint work done in collaboration with FBK between Rasoul Behravesh (FBK), Akhila Rao (RISE), Daniel F Perez-Ramirez (RISE), Davit Harutyunyan (Corporate Research, Robert Bosch GmbH, work done at FBK), Roberto Riggio (Universita Politecnica delle Marche, work done at FBK), and Magnus Boman (KTH).
Here is the abstract and a link to the paper. The code and dataset links are in the Releases page.
“Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.”
Link to the paper: https://ieeexplore.ieee.org/document/9841468