Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services

TL;DR

This paper introduces a novel attention-aware resource allocation framework for Metaverse xURLLC services, aiming to enhance user QoE by optimizing the interaction between service and infrastructure providers and proposing a new QoE metric.

Contribution

It develops a contract-based framework for resource allocation, proposes the Meta-Immersion QoE metric, and validates a 20.1% QoE improvement with attention-aware schemes.

Findings

xURLLC achieves 20.1% higher QoE than conventional URLLC.

The Meta-Immersion metric effectively models user experience.

Attention-aware allocation improves resource efficiency.

Abstract

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users' QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available at https://github.com/HongyangDu/AttentionQoE