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.

该论文提出了一个新颖的服务质量模型，用于具有不同帧率和晕动感要求的异构 360 度视频。我们使用自设计的差异化深度强化学习算法，采用帧槽结构和逐帧优化的方法进行研究。我们设计了两种结构 SIDO 和 MIDO，在这种异构场景中进行了广泛的实验以证明其有效性。