Open AccessMEC‐enabled video streaming in device‐to‐device networks
Author(s) -
Zhang Xuguang,
Lin Huangda,
Chen Mingkai,
Kang Bin,
Wang Lei
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.1198
Subject(s) - goodput , computer science , computer network , scalable video coding , server , cloud computing , scalability , edge computing , transcoding , quality of experience , mobile edge computing , cellular network , wireless network , enhanced data rates for gsm evolution , real time computing , wireless , quality of service , throughput , telecommunications , database , operating system
By offloading video streaming from the centralised cloud to the edge, mobile edge computing (MEC) servers offer new opportunities for real‐time video transmission. Deploying on the edge of users can ensure low latency transmission, however, the limited storage and computing ability cannot adapt to the currently used video transmission technologies such as video transcoding or simulcast. To solve this problem, a more flexible video transmission architecture needs to be considered. Under this motivation, the authors propose a device‐to‐device (D2D) assisted video streaming scheme, which fuses the technical advantages of MEC and scalable video coding. Specifically, they first construct a novel architecture for delay‐sensitive live video streaming services in edge‐enabled wireless heterogeneous networks named MEC‐enabled goodput‐aware (MEGA) model. Then they present a mathematical formulation for optimising the aggregation goodput performance of video traffic including both cellular and D2D links. Finally, they derive a three‐step solution based on a distributed heuristic algorithm. Numerical simulation results show that MEGA outperforms existing models in terms of goodput, end‐to‐end delay, effective loss rate, and users' quality‐of‐experience.