Gao, S.; Wang, Y.; Feng, N.; Wei, Z.; Zhao, J. Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks. Future Internet2023, 15, 184.
Gao, S.; Wang, Y.; Feng, N.; Wei, Z.; Zhao, J. Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks. Future Internet 2023, 15, 184.
Gao, S.; Wang, Y.; Feng, N.; Wei, Z.; Zhao, J. Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks. Future Internet2023, 15, 184.
Gao, S.; Wang, Y.; Feng, N.; Wei, Z.; Zhao, J. Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks. Future Internet 2023, 15, 184.
Abstract
With the proliferating of video surveillance system deployment and related applications, real-time video analysis is very critical to achieve intelligent monitoring, autonomous driving, etc. It is non-trivial to achieve high accuracy and low latency video stream analysis through the traditional cloud computing. In this paper, we propose a non-orthogonal multiple access (NOMA) based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the QoE of UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimension, which respectively select the offloading and resource allocation action, the resolution scaling action. Experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of transmission and computation, and have better performance in convergence compared to other baseline.
Keywords
mobile edge computing (MEC); non-orthogonal multiple access (NOMA); video offloading; resource allocation; deep reinforcement learning (DRL)
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
