Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications
Wireless virtual reality (VR) communication applications have emerged as a transformative technology, offering innovative solutions in various areas of everyday life. However, the successful deployment of these applications faces challenges in ensuring high quality of experience (QoE), especially in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870218/ |
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| author | Georgios Kougioumtzidis Vladimir K. Poulkov Pavlos I. Lazaridis Zaharias D. Zaharis |
| author_facet | Georgios Kougioumtzidis Vladimir K. Poulkov Pavlos I. Lazaridis Zaharias D. Zaharis |
| author_sort | Georgios Kougioumtzidis |
| collection | DOAJ |
| description | Wireless virtual reality (VR) communication applications have emerged as a transformative technology, offering innovative solutions in various areas of everyday life. However, the successful deployment of these applications faces challenges in ensuring high quality of experience (QoE), especially in environments with limited network resources. This research paper presents a novel approach to address the challenge of enhancing QoE by incorporating deep reinforcement learning (DRL) techniques in the resource allocation process. The proposed model takes into account the quality of service (QoS) parameters of the 5G new radio (NR) network to optimize its operation, ensuring a seamless and immersive VR experience. Specifically, the resource allocation strategy adopts a policy that maximizes the transmission-related QoE value based on the evolving characteristics of the communication channel and user interactions. To evaluate the effectiveness of the proposed approach, extensive simulations and comparative analyses against traditional resource allocation methods are performed. The results demonstrate significant improvements in the transmission-related QoE values and highlight the superiority of the DRL-based resource allocation approach in the dynamic and unpredictable wireless environments. |
| format | Article |
| id | doaj-art-640e8c4cfe244ded84a6688bf98ad830 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-640e8c4cfe244ded84a6688bf98ad8302025-02-12T00:02:12ZengIEEEIEEE Access2169-35362025-01-0113250452505810.1109/ACCESS.2025.353854610870218Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR CommunicationsGeorgios Kougioumtzidis0https://orcid.org/0000-0003-2402-3533Vladimir K. Poulkov1https://orcid.org/0000-0003-3226-5639Pavlos I. Lazaridis2https://orcid.org/0000-0001-5091-2567Zaharias D. Zaharis3https://orcid.org/0000-0002-4548-282XDepartment of Communication Networks, Technical University of Sofia, Sofia, BulgariaDepartment of Communication Networks, Technical University of Sofia, Sofia, BulgariaSchool of Computing and Engineering, University of Huddersfield, Huddersfield, U.K.School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceWireless virtual reality (VR) communication applications have emerged as a transformative technology, offering innovative solutions in various areas of everyday life. However, the successful deployment of these applications faces challenges in ensuring high quality of experience (QoE), especially in environments with limited network resources. This research paper presents a novel approach to address the challenge of enhancing QoE by incorporating deep reinforcement learning (DRL) techniques in the resource allocation process. The proposed model takes into account the quality of service (QoS) parameters of the 5G new radio (NR) network to optimize its operation, ensuring a seamless and immersive VR experience. Specifically, the resource allocation strategy adopts a policy that maximizes the transmission-related QoE value based on the evolving characteristics of the communication channel and user interactions. To evaluate the effectiveness of the proposed approach, extensive simulations and comparative analyses against traditional resource allocation methods are performed. The results demonstrate significant improvements in the transmission-related QoE values and highlight the superiority of the DRL-based resource allocation approach in the dynamic and unpredictable wireless environments.https://ieeexplore.ieee.org/document/10870218/5G new radio (NR)deep reinforcement learning (DRL)resource allocationquality of experience (QoE)wireless virtual reality (VR) communications |
| spellingShingle | Georgios Kougioumtzidis Vladimir K. Poulkov Pavlos I. Lazaridis Zaharias D. Zaharis Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications IEEE Access 5G new radio (NR) deep reinforcement learning (DRL) resource allocation quality of experience (QoE) wireless virtual reality (VR) communications |
| title | Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications |
| title_full | Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications |
| title_fullStr | Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications |
| title_full_unstemmed | Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications |
| title_short | Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications |
| title_sort | deep reinforcement learning based resource allocation for qoe enhancement in wireless vr communications |
| topic | 5G new radio (NR) deep reinforcement learning (DRL) resource allocation quality of experience (QoE) wireless virtual reality (VR) communications |
| url | https://ieeexplore.ieee.org/document/10870218/ |
| work_keys_str_mv | AT georgioskougioumtzidis deepreinforcementlearningbasedresourceallocationforqoeenhancementinwirelessvrcommunications AT vladimirkpoulkov deepreinforcementlearningbasedresourceallocationforqoeenhancementinwirelessvrcommunications AT pavlosilazaridis deepreinforcementlearningbasedresourceallocationforqoeenhancementinwirelessvrcommunications AT zahariasdzaharis deepreinforcementlearningbasedresourceallocationforqoeenhancementinwirelessvrcommunications |