Junchao Yang , Ali Kashif Bashir , Zhiwei Guo , Keping Yu , Mohsen Guizani
{"title":"Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks","authors":"Junchao Yang , Ali Kashif Bashir , Zhiwei Guo , Keping Yu , Mohsen Guizani","doi":"10.1016/j.dcan.2023.07.003","DOIUrl":null,"url":null,"abstract":"<div><div>Virtual Reality (VR) is a key industry for the development of the digital economy in the future. Mobile VR has advantages in terms of mobility, lightweight and cost-effectiveness, which has gradually become the mainstream implementation of VR. In this paper, a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing (MEC)-equipped 5G networks is proposed, aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission. To support <span>VR</span> content proactive caching and intelligent buffer management, users' behavioral similarity and head movement trajectory are jointly used for viewpoint prediction. The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content. Second, a hierarchical buffer-based adaptive update algorithm is presented, which jointly considers bandwidth, buffer, and predicted viewpoint status to update the tile chunk in client buffer. Then, according to the decomposition of the problem, the buffer update problem is modeled as an optimization problem, and the corresponding solution algorithms are presented. Finally, the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations, and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 5","pages":"Pages 1234-1244"},"PeriodicalIF":7.5000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Communications and Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352864823001190","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Virtual Reality (VR) is a key industry for the development of the digital economy in the future. Mobile VR has advantages in terms of mobility, lightweight and cost-effectiveness, which has gradually become the mainstream implementation of VR. In this paper, a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing (MEC)-equipped 5G networks is proposed, aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission. To support VR content proactive caching and intelligent buffer management, users' behavioral similarity and head movement trajectory are jointly used for viewpoint prediction. The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content. Second, a hierarchical buffer-based adaptive update algorithm is presented, which jointly considers bandwidth, buffer, and predicted viewpoint status to update the tile chunk in client buffer. Then, according to the decomposition of the problem, the buffer update problem is modeled as an optimization problem, and the corresponding solution algorithms are presented. Finally, the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations, and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.
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