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.

翻译：先进的视频技术正在推动未来元宇宙的发展，旨在随时随地连接用户。因此，用户的应用场景将更加多样化，导致非VR与VR两类360度视频的混合。本文针对帧率与晕动症需求不同的异质化360度视频，提出了一种新型服务质量模型。我们设计了一种帧时隙结构，并利用自设计的差异化深度强化学习算法实现逐帧优化。具体而言，针对该异质化场景，我们设计了两种结构：分离输入差异输出（SIDO）与合并输入差异输出（MIDO）。通过综合实验验证了其有效性。