Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection
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arXiv
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| Format: | Preprint |
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2024
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| author | Iturria-Rivera, Pedro Enrique Gaigalas, Raimundas Elsayed, Medhat Bavand, Majid Ozcan, Yigit Erol-Kantarci, Melike |
| author_facet | Iturria-Rivera, Pedro Enrique Gaigalas, Raimundas Elsayed, Medhat Bavand, Majid Ozcan, Yigit Erol-Kantarci, Melike |
| contents | Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These applications impose significant challenges on network infrastructure, which can be addressed by machine learning algorithms due to their adaptability. This paper presents a Multi- Agent Reinforcement Learning (MARL) solution for optimizing codec parameters of XR traffic, comparing it to the Adjust Packet Size (APS) algorithm. Our cooperative multi-agent system uses an Optimistic Mixture of Q-Values (oQMIX) approach for handling Cloud Gaming (CG), Augmented Reality (AR), and Virtual Reality (VR) traffic. Enhancements include an attention mechanism and slate-Markov Decision Process (MDP) for improved action selection. Simulations show our solution outperforms APS with average gains of 30.1%, 15.6%, 16.5% 50.3% in XR index, jitter, delay, and Packet Loss Ratio (PLR), respectively. APS tends to increase throughput but also packet losses, whereas oQMIX reduces PLR, delay, and jitter while maintaining goodput. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection Iturria-Rivera, Pedro Enrique Gaigalas, Raimundas Elsayed, Medhat Bavand, Majid Ozcan, Yigit Erol-Kantarci, Melike Networking and Internet Architecture Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These applications impose significant challenges on network infrastructure, which can be addressed by machine learning algorithms due to their adaptability. This paper presents a Multi- Agent Reinforcement Learning (MARL) solution for optimizing codec parameters of XR traffic, comparing it to the Adjust Packet Size (APS) algorithm. Our cooperative multi-agent system uses an Optimistic Mixture of Q-Values (oQMIX) approach for handling Cloud Gaming (CG), Augmented Reality (AR), and Virtual Reality (VR) traffic. Enhancements include an attention mechanism and slate-Markov Decision Process (MDP) for improved action selection. Simulations show our solution outperforms APS with average gains of 30.1%, 15.6%, 16.5% 50.3% in XR index, jitter, delay, and Packet Loss Ratio (PLR), respectively. APS tends to increase throughput but also packet losses, whereas oQMIX reduces PLR, delay, and jitter while maintaining goodput. |
| title | Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2405.15872 |