Attention-aware resource allocation and QoE analysis for metaverse xURLLC services

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 i...

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Main Authors: Du, Hongyang, Liu, Jiazhen, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Zhang, Junshan, Kim, Dong In
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172580
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1725802023-12-13T06:39:43Z Attention-aware resource allocation and QoE analysis for metaverse xURLLC services Du, Hongyang Liu, Jiazhen Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Junshan Kim, Dong In School of Computer Science and Engineering Engineering::Computer science and engineering Metaverse Contract Theory 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. Info-communications Media Development Authority (IMDA) Ministry of Education (MOE) National Research Foundation (NRF) This work was supported in part by NSFC under Grant 62102099 and Grant U22A2054; in part by the Pearl River Talent Recruitment Program under Grant 2021QN02S643; in part by the National Key Research and Development Program of China under Grant 2020YFB1807802; in part by the National Research Foundation (NRF), Singapore, and Infocomm Media Development Authority under the Future Communications Research Development Programme (FCP); in part by the DSO National Laboratories under the AI Singapore Programme (AISG) under the Energy Research Test-Bed and Industry Partnership Funding Initiative under Award AISG2-RP2020-019; in part by the Energy Grid (EG) 2.0 Programme under DesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) Programme; in part by the National Research Foundation (NRF) and Infocomm Media Development Authority under the Future Communications Research Development Programme (FCP); in part by the Singapore University of Technology and Design (SUTD) under Grant SRG-ISTD-2021-165; in part by SUTD-Zhejiang University Innovation, Design and Entrepreneurship Alliance (ZJU IDEA) under Grant SUTD-ZJU (VP) 202102; in part by the Ministry of Education, Singapore, under its SUTD Kickstarter Initiative under Grant SKI 20210204; in part by NSF under Grant CNS-2203239 and Grant CNS-2203412; and in part by the Ministry of Science and ICT (MSIT), South Korea, under the ICT Creative Consilience Program supervised by Institute of Information and Communications Technology Planning and Evaluation (IITP) under Grant IITP-2020-0-01821. 2023-12-13T06:39:43Z 2023-12-13T06:39:43Z 2023 Journal Article Du, H., Liu, J., Niyato, D., Kang, J., Xiong, Z., Zhang, J. & Kim, D. I. (2023). Attention-aware resource allocation and QoE analysis for metaverse xURLLC services. IEEE Journal On Selected Areas in Communications, 41(7), 2158-2175. https://dx.doi.org/10.1109/JSAC.2023.3280978 0733-8716 https://hdl.handle.net/10356/172580 10.1109/JSAC.2023.3280978 2-s2.0-85151338992 7 41 2158 2175 en AISG2-RP-2020-019 SRG-ISTD-2021-165 SUTD-ZJU (VP) 202102 SKI 20210204 IEEE Journal on Selected Areas in Communications © 2023 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Metaverse
Contract Theory
spellingShingle Engineering::Computer science and engineering
Metaverse
Contract Theory
Du, Hongyang
Liu, Jiazhen
Niyato, Dusit
Kang, Jiawen
Xiong, Zehui
Zhang, Junshan
Kim, Dong In
Attention-aware resource allocation and QoE analysis for metaverse xURLLC services
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Du, Hongyang
Liu, Jiazhen
Niyato, Dusit
Kang, Jiawen
Xiong, Zehui
Zhang, Junshan
Kim, Dong In
format Article
author Du, Hongyang
Liu, Jiazhen
Niyato, Dusit
Kang, Jiawen
Xiong, Zehui
Zhang, Junshan
Kim, Dong In
author_sort Du, Hongyang
title Attention-aware resource allocation and QoE analysis for metaverse xURLLC services
title_short Attention-aware resource allocation and QoE analysis for metaverse xURLLC services
title_full Attention-aware resource allocation and QoE analysis for metaverse xURLLC services
title_fullStr Attention-aware resource allocation and QoE analysis for metaverse xURLLC services
title_full_unstemmed Attention-aware resource allocation and QoE analysis for metaverse xURLLC services
title_sort attention-aware resource allocation and qoe analysis for metaverse xurllc services
publishDate 2023
url https://hdl.handle.net/10356/172580
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