Enhancing video QoE over high-speed train using segment-based prefetching and caching

Yue Cao, Ning Wang, Celimuge Wu, Xu Zhang, Chakkaphong Suthaputchakun

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


The big picture of 5G will bring a range of new unique service capabilities, where ensuring Quality of Experience (QoE) continuity in challenging situations such as high mobility, e.g., on-board user equipments (UEs) in high-speed train (HST) are one of the sharp killer applications. In this paper, we propose a mobile edge computing (MEC) driven solution to improve the QoE for UEs in the HST with perceived dynamic adaptive streaming over HTTP video demands. Considering the challenging wireless communication conditioning (e.g., path loss and Doppler Effect due to high mobility) between HST and base station along the railway for enabling progress and seamless video consuming, the case study shows the benefit of MEC functions mainly from content prefetching and complementarily from content caching, over benchmark solution where UEs solely download video segments through challenging wireless channel.

Original languageEnglish
Pages (from-to)55-66
Number of pages12
JournalIEEE MultiMedia
Issue number4
Early online date15 Mar 2019
Publication statusPublished - 1 Oct 2019

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