Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
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Abstract (arXiv)
Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime. As such, the use cases for users will be much more diverse, leading to a mix of 360-degree videos with two types: non-VR and VR 360-degree videos. This paper presents a novel Quality of Service model for heterogeneous 360-degree videos with different requirements for frame rates and cybersickness. We propose a frame-slotted structure and conduct frame-wise optimization using self-designed differentiated deep reinforcement learning algorithms. Specifically, we design two structures, Separate Input Differentiated Output (SIDO) and Merged Input Differentiated Output (MIDO), for this heterogeneous scenario. We also conduct comprehensive experiments to demonstrate their effectiveness.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 8 Aug 2023.
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References · 8
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
- 2018Is Q-learning Provably Efficient?cited by 485
- 2020Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorialcited by 59
- 2017Hybrid Reward Architecture for Reinforcement Learningcited by 57
- 2021DeepWiVe: Deep-Learning-Aided Wireless Video Transmissioncited by 45
- 2022Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learningcited by 16
- 2022Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communicationscited by 9
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