Real-time Extended Reality Video Transmission Optimization Based on Frame-priority Scheduling
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929237650309120 |
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| author | Pan, Guangjin Xu, Shugong Zhang, Shunqing Chen, Xiaojing Sun, Yanzan |
| author_facet | Pan, Guangjin Xu, Shugong Zhang, Shunqing Chen, Xiaojing Sun, Yanzan |
| contents | Extended reality (XR) is one of the most important applications of 5G. For real-time XR video transmission in 5G networks, a low latency and high data rate are required. In this paper, we propose a resource allocation scheme based on frame-priority scheduling to meet these requirements. The optimization problem is modelled as a frame-priority-based radio resource scheduling problem to improve transmission quality. We propose a scheduling framework based on multi-step Deep Q-network (MS-DQN) and design a neural network model based on convolutional neural network (CNN). Simulation results show that the scheduling framework based on frame-priority and MS-DQN can improve transmission quality by 49.9%-80.2%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-time Extended Reality Video Transmission Optimization Based on Frame-priority Scheduling Pan, Guangjin Xu, Shugong Zhang, Shunqing Chen, Xiaojing Sun, Yanzan Networking and Internet Architecture Multimedia Signal Processing Extended reality (XR) is one of the most important applications of 5G. For real-time XR video transmission in 5G networks, a low latency and high data rate are required. In this paper, we propose a resource allocation scheme based on frame-priority scheduling to meet these requirements. The optimization problem is modelled as a frame-priority-based radio resource scheduling problem to improve transmission quality. We propose a scheduling framework based on multi-step Deep Q-network (MS-DQN) and design a neural network model based on convolutional neural network (CNN). Simulation results show that the scheduling framework based on frame-priority and MS-DQN can improve transmission quality by 49.9%-80.2%. |
| title | Real-time Extended Reality Video Transmission Optimization Based on Frame-priority Scheduling |
| topic | Networking and Internet Architecture Multimedia Signal Processing |
| url | https://arxiv.org/abs/2402.01180 |