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Content-driven Joint Allocation of Communication and Computing Resources in Vehicular Networks
Xu Zhu,Fangfang Liu,Zhimin Zeng,Caili Guo,Jiujiu Chen 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
With the rapid growth of computer vision applications, a large amount of video data in the Internet of Vehicles scenario are used for content analysis. Tasks based on video content understanding are usually accompanied by huge amount of calculation, which put great pressure on traditional wireless communication resource and Mobile Edge Computing (MEC) server computing resource. Furthermore, existing resource allocation schemes based on Quality of Service (QoS) or Quality of Experience (QoE) may not be the best choice for the purpose of video content understanding. In this paper, we propose a joint resource allocation scheme based on Quality of Content (QoC) to maximize the accuracy of video content understanding. Due to the real-time nature of resource allocation and the variability of the environment in autonomous driving scenarios, we design a Multi-agent Distributed Q-Learning algorithm to solve such multi-constrained nonlinear programming problems. Finally, the simulation results show that our proposed QoC-based joint resource allocation scheme has better video content understanding performance.
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