  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.
