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

As a virtual world interacting with the real world, Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Conventional ultra-reliable and low-latency communications (URLLC) can satisfy the vast majority of objective service KPIs, but it is difficult to offer users a personalized experience in Metaverse services. Since increasing the quality of experience (QoE) can be regarded as an imperative KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) to support Metaverse services based on graphical technology. By allocating more resources to virtual objects in which users are more interested, a higher QoE can be achieved. In this paper, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP) to deploy Metaverse xURLLC services. An optimal contract design framework is provided. 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 of Metaverse xURLLC services, we propose a novel metric named Meta-Immersion that incorporates both the objective network KPIs and subjective feelings of Metaverse users. Using a user-object-attention level (UOAL) dataset, we develop and validate an attention-aware rendering capacity allocation scheme to improve QoE. It is shown that an average of 20.1% QoE improvement is achieved by the xURLLC compared to the conventional URLLC with the uniform allocation scheme. A higher percentage of QoE improvement, e.g., 40%, is achieved when the total resources are limited.