元宇宙中的异构360度视频：差异化强化学习方法
Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
AI总结：
本文针对元宇宙中非VR与VR 360度视频混合传输场景，提出基于帧时隙结构的差异化深度强化学习方法，设计SIDO和MIDO两种结构进行逐帧优化，并通过实验验证其在满足不同帧率和晕动症要求方面的有效性。
摘要：
先进的视频技术正在推动未来元宇宙的发展，元宇宙旨在随时随地连接用户。因此，用户的使用场景将更加多样化，导致360度视频混合存在两种类型：非VR和VR 360度视频。本文针对具有不同帧率和晕动症要求的异构360度视频，提出了一种新颖的服务质量模型。我们提出了一种帧时隙结构，并利用自行设计的差异化深度强化学习算法进行逐帧优化。具体而言，我们为这种异构场景设计了两种结构：分离输入差异化输出（SIDO）和合并输入差异化输出（MIDO）。我们还进行了全面的实验以证明它们的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
