Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches

TL;DR

This paper introduces a novel QoS model for heterogeneous 360-degree videos in the Metaverse, utilizing differentiated deep reinforcement learning algorithms to optimize frame delivery based on diverse user requirements.

Contribution

It proposes two innovative reinforcement learning structures, SIDO and MIDO, for optimizing heterogeneous 360-degree video streaming in the Metaverse.

Findings

Both SIDO and MIDO outperform baseline methods in experiments.

The models effectively adapt to different video types and user requirements.

Enhanced QoS and reduced cybersickness in VR environments.

Abstract

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.