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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 quality of experience (QoE) of the 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 dimensionality, which, respectively, select the offloading and resource allocation action, as well as the resolution scaling action. The experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of edge computing and has better performance in terms of convergence compared to other baseline schemes.<\/jats:p>","DOI":"10.3390\/fi15050184","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:55:29Z","timestamp":1684457729000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Siyu","family":"Gao","sequence":"first","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2917-1976","authenticated-orcid":false,"given":"Zhongcheng","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jijun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108146","DOI":"10.1016\/j.patcog.2021.108146","article-title":"Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles","volume":"121","author":"Wan","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_2","unstructured":"Cisco (2023, March 22). 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