Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/5/184 |
| _version_ | 1797600039345848320 |
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| author | Siyu Gao Yuchen Wang Nan Feng Zhongcheng Wei Jijun Zhao |
| author_facet | Siyu Gao Yuchen Wang Nan Feng Zhongcheng Wei Jijun Zhao |
| author_sort | Siyu Gao |
| collection | DOAJ |
| description | With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. 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. |
| first_indexed | 2024-03-11T03:42:48Z |
| format | Article |
| id | doaj.art-9d262a09bcb24da58b26548c1fab788a |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T03:42:48Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-9d262a09bcb24da58b26548c1fab788a2023-11-18T01:27:15ZengMDPI AGFuture Internet1999-59032023-05-0115518410.3390/fi15050184Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled NetworksSiyu Gao0Yuchen Wang1Nan Feng2Zhongcheng Wei3Jijun Zhao4School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, ChinaWith the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. 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.https://www.mdpi.com/1999-5903/15/5/184mobile edge computing (MEC)non-orthogonal multiple access (NOMA)video offloadingresource allocationdeep reinforcement learning (DRL) |
| spellingShingle | Siyu Gao Yuchen Wang Nan Feng Zhongcheng Wei Jijun Zhao Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks Future Internet mobile edge computing (MEC) non-orthogonal multiple access (NOMA) video offloading resource allocation deep reinforcement learning (DRL) |
| title | Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks |
| title_full | Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks |
| title_fullStr | Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks |
| title_full_unstemmed | Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks |
| title_short | Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks |
| title_sort | deep reinforcement learning based video offloading and resource allocation in noma enabled networks |
| topic | mobile edge computing (MEC) non-orthogonal multiple access (NOMA) video offloading resource allocation deep reinforcement learning (DRL) |
| url | https://www.mdpi.com/1999-5903/15/5/184 |
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