| Title |
Follow-me Prefetching for Video Streaming over Mobile Edge Computing Networks |
| ID_Doc |
44180 |
| Authors |
Mohammedameen, IS; Mkwawa, IH; Sun, LF |
| Title |
Follow-me Prefetching for Video Streaming over Mobile Edge Computing Networks |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00339 |
| Abstract |
Mobile video streaming services have increased exponentially in recent years due to the popularity of mobile devices, the advancement of mobile networks and the availability of a variety of video contents over the Internet. Mobile Edge Computing (MEC), in connection with the backend cloud computing, has been used to bring contents close to the end user in order to reduce transmission latency. However, the quality of video streaming services suffers from degradation when an end user moves from the coverage of one node to another or when the condition of a mobile network degrades. In this paper, we propose a novel follow-me Edge Node Prefetching (ENP) scheme to prefetch appropriate video segments in advance in the followingon mobile node to avoid video quality degradation during video streaming. We set up a test bed consisting of a back-end cloud (OpenStack), two edge nodes (LXD Containers) and a mobile device, and implemented the ENP algorithms on cloud server and client sides. Extensive experiments for Dynamic Adaptive video Streaming over HTTP (DASH) services were carried out based on dash.js from the DASH Industry Forum. Preliminary results show that the ENP scheme can achieve better video quality (in terms of provisioning of average video bit rate per segment) and less service migration time between mobile nodes when compared with existing approaches. The scheme might be useful in supporting video streaming services over MEC, and/or in future smart city applications. |
| Author Keywords |
QoE; MEC; MPEG-DASH; prefetching; Migration |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000936421900287 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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