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Emerging with the support of computing and communications technologies,Metaverse is expected to bring users unprecedented service experiences.However, the increase in the number of Metaverse users places a heavy demand onnetwork resources, especially for Metaverse services that are based ongraphical extended reality and require rendering a plethora of virtual objects.To make efficient use of network resources and improve theQuality-of-Experience (QoE), we design an attention-aware network resourceallocation scheme to achieve customized Metaverse services. The aim is toallocate more network resources to virtual objects in which users are moreinterested. We first discuss several key techniques related to Metaverseservices, including QoE analysis, eye-tracking, and remote rendering. We thenreview existing datasets and propose the user-object-attention level (UOAL)dataset that contains the ground truth attention of 30 users to 96 objects in1,000 images. A tutorial on how to use UOAL is presented. With the help ofUOAL, we propose an attention-aware network resource allocation algorithm thathas two steps, i.e., attention prediction and QoE maximization. Specially, weprovide an overview of the designs of two types of attention predictionmethods, i.e., interest-aware and time-aware prediction. By using the predicteduser-object-attention values, network resources such as the rendering capacityof edge devices can be allocated optimally to maximize the QoE. Finally, wepropose promising research directions related to Metaverse services.

Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services