Live MPEG-DASH video streaming cache management with cognitive mobile edge computing

Hung Yen Weng, Ren Hung Hwang, Chin Feng Lai

Research output: Contribution to journalArticlepeer-review

Abstract

Video streaming is expected to account for up to 82% of network traffic by 2021 according to the forecast of CISCO's Visual Networking Index. Dynamic Adaptive Streaming over HTTP (DASH) is the de facto protocol for delivering video streaming services over the Internet. As the cellular network entering the 5G era, more and more video streaming will be live video streaming delivered to mobile phones. However, due to a large amount of video traffic, several techniques are required to improve the user Quality of Experience (QoE). In this work, we first propose a network architecture design for delivering live video streaming over the cellular core network with cognitive Mobile Edge Computing (MEC) servers. We then focused on the optimal cache management by considering several issues, include QoE, cache size, backhaul bandwidth, pre-cache mechanism, and user mobility. Finally, we show a prototype of the proposed MEC-assisted live video streaming system. Our simulation results show the performance improvement of the proposed cache management schemes in terms of QoE-based system utility. Our prototype shows the significant latency reduction in receiving video streams with MEC pre-cache mechanism.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
DOIs
Publication statusAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Fingerprint Dive into the research topics of 'Live MPEG-DASH video streaming cache management with cognitive mobile edge computing'. Together they form a unique fingerprint.

Cite this