MetaCast: A Self-Driven Metaverse Announcer Architecture Based on Quality of Experience Evaluation Model

TL;DR

MetaCast introduces a self-driven metaverse announcer system that identifies events, positions cameras, and blends shots, optimizing user experience through a novel QoE evaluation model and practical implementation in a campus metaverse.

Contribution

The paper presents a three-stage architecture for metaverse announcers and a MAUE model to enhance user experience, along with a practical implementation and user study.

Findings

Effective announcer settings aligned with user preferences

Satisfactory QoE achieved through optimized parameters

Validated system in a university campus metaverse prototype

Abstract

Metaverse provides users with a novel experience through immersive multimedia technologies. Along with the rapid user growth, numerous events bursting in the metaverse necessitate an announcer to help catch and monitor ongoing events. However, systems on the market primarily serve for esports competitions and rely on human directors, making it challenging to provide 24-hour delivery in the metaverse persistent world. To fill the blank, we proposed a three-stage architecture for metaverse announcers, which is designed to identify events, position cameras, and blend between shots. Based on the architecture, we introduced a Metaverse Announcer User Experience (MAUE) model to identify the factors affecting the users' Quality of Experience (QoE) from a human-centered perspective. In addition, we implemented \textit{MetaCast}, a practical self-driven metaverse announcer in a university campus metaverse prototype, to conduct user studies for MAUE model. The experimental results have effectively achieved satisfactory announcer settings that align with the preferences of most users, encompassing parameters such as video transition rate, repetition rate, importance threshold value, and image composition.