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Machine Learning Assisted Video Stream Offloading for 5G MBMS Mobile Edge Computing

delete2023-12-01
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PRE
AI
J
Junsheng Mu
J
Jian Jin
X
Xiaojun Jing
R
Ronghui Zhang *
张培颖 封面图
张培颖 (Peiying Zhang)
H
Hailong Zhu
DOI:10.1109/TBC.2023.3294837delete
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摘要

摘要

En 中文
With the advancement of multimedia technology, 5G MBMS has become a promising technology that makes full and effective use of network resources to provide services. Mobile edge computing (MEC) is a key technology that brings important improvements to MBMS by utilizing contextual information such as multimedia, virtual reality, etc. However, the deployment of MEC may bring the latency to the network, which requires the deployment consideration of computing nodes. Moreover, mobile network operators need to consider the number of base stations required to accommodate the upcoming MBMS data streams. In this paper, we propose a computational offloading strategy and a co-optimization mechanism of video resource assignment in an edge cloud server enabled MBMS service system. Our approach aims to maximize computation speed while satisfying limited resource constraints, constrained energy consumption, and minimum latency time requirements, in order to provide better MBMS services. Additionally, our approach offers an advanced solution for achieving optimal computational offloading and resource allocation in edge cloud server enabled MBMS service systems. Extensive simulation results demonstrate that the proposed mechanism outperforms current state-of-the-art schemes. Specifically, the total calculation rate of the large-scale cloud server is significantly reduced under energy constraints, total delay requirements, and different network parameters.
Keyword:
MBMS
multimedia services
computation offloading
mobile edge computing
resource allocation

期刊

IEEE Transactions on Broadcasting 封面图
IEEE Transactions on Broadcasting
IF:
4.8
论文数:
2.1K
被引数:
3.0K

机构

B
beijing university of posts & telecommunications
学者数:
1.4W
论文数: 1.2W
被引数: 9
C
china academy of information & communication technology
学者数:
332
论文数: 271
被引数: 0
C
china university of petroleum
学者数:
4.1W
论文数: 2.7W
被引数: 30
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