Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services
Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services
复制标题
探索定制元界服务的注意力感知网络资源分配
作者：
H. Du;Jiacheng Wang;D. Niyato;Jiawen Kang;Zehui Xiong;X. Shen;Dong In Kim
在计算和通信技术的支持下，Metaverse有望为用户带来前所未有的服务体验。然而，Metaverse用户数量的增加对网络资源提出了沉重的需求，特别是对于基于图形延展实境并需要渲染过多虚拟对象的Metaverse服务。为了有效利用网络资源，提高用户体验质量，设计了一种注意力感知的网络资源分配方案，实现Metaverse的定制服务。其目的是将更多的网络资源分配给用户更感兴趣的虚拟对象。我们首先讨论了与Metaverse服务相关的几个关键技术，包括QoE分析，眼动跟踪和远程渲染。然后，我们回顾了现有的数据集，并提出了用户-对象-注意力水平（UOAL）数据集，它包含了1000张图像中30个用户对96个对象的真实注意力。介绍了如何使用UOAL的教程。在UOAL的帮助下，我们提出了一种注意力感知的网络资源分配算法，该算法分为两个步骤，即，注意力预测和QoE最大化。特别地，我们提供了两种类型的注意预测方法的设计的概述，即，兴趣感知和时间感知预测。通过使用预测的用户对象关注值，可以最优地分配诸如边缘设备的渲染能力的网络资源以最大化QoE。最后，我们提出了与Metaverse服务相关的有前途的研究方向。
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 on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attention-aware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-object-attention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1, 000 images. A tutorial on how to use UOAL is presented. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interest-aware and time-aware prediction. By using the predicted user-object-attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services.