Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse

XU Siya; FU Qimeng; GUO Shaoyong

doi:10.11959/j.issn.1000-436x.2024187

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Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse

Papers | 更新时间：2024-11-14

- Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse
- Journal on Communications Vol. 45, Issue 10, Pages: 55-68(2024)
- 作者机构：
  
  北京邮电大学网络与交换技术全国重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62201074);The Basic Research Fund From Beijing University of Posts and Telecommunications(2023ZCTH11)
- DOI：10.11959/j.issn.1000-436x.2024187
  CLC： TP393
- Received：02 August 2024，
  
  Revised：2024-10-11，
  
  Published：25 October 2024
- 稿件说明：
移动端阅览
徐思雅,付琦梦,郭少勇.面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制[J].通信学报,2024,45(10):55-68.

XU Siya,FU Qimeng,GUO Shaoyong.Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse[J].Journal on Communications,2024,45(10):55-68.
徐思雅,付琦梦,郭少勇.面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制[J].通信学报,2024,45(10):55-68. DOI： 10.11959/j.issn.1000-436x.2024187.

XU Siya,FU Qimeng,GUO Shaoyong.Proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the Metaverse[J].Journal on Communications,2024,45(10):55-68. DOI： 10.11959/j.issn.1000-436x.2024187.

摘要

针对移动增强现实（MAR）应用异质数据内容传输和响应时延的问题，提出了面向元宇宙移动增强现实应用的异质内容主动缓存与个性化交付机制，首先综合考虑了用户特征和边缘节点服务能力，提出了用户行为和资源感知的边缘协作服务域构建方法；进而，基于协作服务域设计基于存储空间划分和用户偏好预测的异质内容预缓存机制；特别地，针对前景内容引入了个性化推荐机制，并设计了前景内容的差异化策略。仿真结果表明，所提机制在缓存命中率、前景内容和异质内容的平均响应时延方面均优于NCPCR策略、CCS-AGP策略和AIEC-RSC策略。

Abstract

Addressing the issues of heterogeneous data content transmission and response latency in mobile augmented reality (MAR) applications

a proactive caching and personalized delivery mechanism of heterogeneous content for MAR applications in the metaverse was proposed. Firstly

considering user characteristics and edge node service capabilities

a user behavior and resource aware edge collaboration service domain (ECSD) construction method was proposed. Then

based on the ECSD

heterogeneous content pre-caching mechanisms based on storage space division and user preference prediction were designed. Specially

by introducing the personalized recommendation mechanism of foreground content

a differentiated delivery strategy of foreground content was designed. Simulation results show that the proposed mechanism outperforms NCPCR

CCS-AGP

and AIEC-RSC strategy in terms of cache hit rate

average response latency for foreground content and heterogeneous content.

关键词

Keywords

references

XU M R , NG W C , LIM W Y B , et al . A full dive into realizing the edge-enabled metaverse: visions, enabling technologies, and challenges [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 1 ): 656 - 700 .

SIRIWARDHANA Y , PORAMBAGE P , LIYANAGE M , et al . A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects [J ] . IEEE Communications Surveys & Tutorials , 2021 , 23 ( 2 ): 1160 - 1192 .

HUANG Z H , FRIDERIKOS V . Mobility aware optimization in the metaverse [C ] // Proceedings of the 2022 IEEE Globecom Workshops (GC Wkshps) . Piscataway : IEEE Press , 2022 : 80 - 86 .

HUANG Z H , FRIDERIKOS V . Optimal mobility-aware wireless edge cloud support for the metaverse [J ] . Future Internet , 2023 , 15 ( 2 ): 47 .

SI P Y , ZHAO J , HAN H M , et al . Resource allocation and resolution control in the metaverse with mobile augmented reality [C ] // Proceedings of the GLOBECOM 2022 - 2022 IEEE Global Communications Conference . Piscataway : IEEE Press , 2022 : 3265 - 3271 .

ZHANG L , WU X M , WANG F , et al . Edge-based video stream generation for multi-party mobile augmented reality [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 1 ): 409 - 422 .

CHEN X , LIU G Z . Energy-efficient task offloading and resource allocation via deep reinforcement learning for augmented reality in mobile edge networks [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 13 ): 10843 - 10856 .

SOMESULA M K , ROUT R R , SOMAYAJULU D V L N . Greedy cooperative cache placement for mobile edge networks with user preferences prediction and adaptive clustering [J ] . Ad Hoc Networks , 2023 , 140 : 103051 .

WANG Y , YU T , SAKAGUCHI K . Context-based MEC platform for augmented-reality services in 5G networks [C ] // Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) . Piscataway : IEEE Press , 2021 : 1 - 5 .

XU Z C , YUAN Z , LIANG W F , et al . Learning-driven algorithms for responsive AR offloading with non-deterministic rewards in metaverse-enabled MEC [J ] . IEEE/ACM Transactions on Networking , 2024 , 32 ( 2 ): 1556 - 1572 .

FU Y R , SALAÜN L , YANG X L , et al . Caching efficiency maximization for device-to-device communication networks: a recommend to cache approach [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 10 ): 6580 - 6594 .

龙隆 , 刘子辰 , 陆在旺 , 等 . 移动边缘网络下服务缓存与资源分配联合优化策略 [J ] . 通信学报 , 2023 , 44 ( 1 ): 64 - 74 .

LONG L , LIU Z C , LU Z W , et al . Joint optimization strategy of service cache and resource allocation in mobile edge network [J ] . Journal on Communications , 2023 , 44 ( 1 ): 64 - 74 .

SEO Y J , LEE J , HWANG J , et al . A novel joint mobile cache and power management scheme for energy-efficient mobile augmented reality service in mobile edge computing [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 5 ): 1061 - 1065 .

ZENG F , ZHANG K W , WU L , et al . Efficient caching in vehicular edge computing based on edge-cloud collaboration [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 2 ): 2468 - 2481 .

XU S Y , CHI J Y , WANG S , et al . AIEC-RSC: AI and edge collaboration empowered reliable service computing for high-speed mobile businesses [J ] . IEEE Transactions on Services Computing , 2024 , 17 ( 1 ): 224 - 236 .

CHI J Y , XU S Y , GUO S Y , et al . Federated learning empowered edge collaborative content caching mechanism for Internet of vehicles [C ] // Proceedings of the NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium . Piscataway : IEEE Press , 2022 : 1 - 5 .

SONG M Y , SHAN H G , FU Y R , et al . Joint user-side recommendation and D2D-assisted offloading for cache-enabled cellular networks with mobility consideration [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 11 ): 8080 - 8095 .

DANG T N , KIM K , KHAN L U , et al . On-device computational caching-enabled augmented reality for 5G and beyond: a contract-theory-based incentive mechanism [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 24 ): 17382 - 17394 .

李云 , 高倩 , 姚枝秀 , 等 . 移动边缘计算中智能服务编排和算网资源分配联合优化方法 [J ] . 通信学报 , 2023 , 44 ( 7 ): 51 - 63 .

LI Y , GAO Q , YAO Z X , et al . Joint optimization method of intelligent service arrangement and computing-networking resource allocation for MEC [J ] . Journal on Communications , 2023 , 44 ( 7 ): 51 - 63 .

WU Z Y , YAN D F . Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network [J ] . China Communications , 2021 , 18 ( 11 ): 26 - 41 .

CAO Z , ZHOU P , LI R , et al . Multiagent deep reinforcement learning for joint multichannel access and task offloading of mobile-edge computing in industry 4.0 [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 7 ): 6201 - 6213 .

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