QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR

doi:10.1109/TVT.2025.3558337

科研成果详情

题名	QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR
作者	Li，Yang 1; Wu，Yuan 2; Xing，Huanlai 1; Feng，Li 1; Wang，Tian 3; Jia，Weijia 3,4
发表日期	2025
发表期刊	IEEE Transactions on Vehicular Technology
ISSN/eISSN	0018-9545
摘要	Augmented reality (AR) enhances human perception by overlaying digital information in the real environment and can be applied to various fields such as education, healthcare, and industry. To relieve the computing pressure of resourcelimited AR devices, edge assisted AR has attracted significant attention in recent years. It enables AR devices to offload the computation-intensive environment analysis tasks to the edge server and to download the point-based 3D virtual objects for rendering. Although this method can effectively reduce the processing latency, it may introduce a long transmission latency. To address this issue, this work proposes a semantic communication empowered edge assisted AR to reduce the quality of experience (QoE)-aware latency. To this end, the AR devices first utilize an image encoder to extract semantic features of source images. Due to the importance difference of extracted features and limited communication resources, the feature selection and bandwidth allocation are explored to improve transmission efficiency. In addition, when the edge server completes the generation of pointbased 3D virtual objects, it employs a transformer-based encoder to mask a proportion of points and learn a high-level representation of unmasked points. In this way, low-complexity semantic information of 3D objects is sent to the AR devices, which can reduce the transmission overhead of 3D objects. Moreover, the mask ratio of the point encoder is further optimized to balance the reconstructed quality and transmission latency of 3D objects. Therefore, this work aims to minimize the overall QoE-aware latency of edge assisted AR by jointly optimizing the semantic feature selection, mask ratio, and bandwidth allocation. A convexbased improved Harris Hawks optimization algorithm is designed to solve the formulated problem. Experimental results demonstrate that our semantic communication empowered edge assisted AR can effectively reduce the overall latency and outperform the other baselines
关键词	and QoE-aware Latency Minimization Bandwidth Allocation Edge Assisted AR Semantic Communication
DOI	10.1109/TVT.2025.3558337
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-105002131967
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/13739
专题	北师香港浸会大学
通讯作者	Li，Yang
作者单位	1.Southwest Jiaotong University,School of Computer and Artificial Intelligent,Chengdu,611756,China 2.University of Macau,State Key Laboratory of Internet of Things for Smart City,Department of Computer and Information Science,Macau,Macao 3.Beijing Normal University,Institute of Artificial Intelligence and Future Networks,Zhuhai,519087,China 4.Guangdong Key Lab of AI and Multi-Modal Data Processing,BNU-HKBU United International College,Zhuhai,519087,China
推荐引用方式 GB/T 7714	Li，Yang,Wu，Yuan,Xing，Huanlaiet al. QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR[J]. IEEE Transactions on Vehicular Technology, 2025.
APA	Li，Yang, Wu，Yuan, Xing，Huanlai, Feng，Li, Wang，Tian, & Jia，Weijia. (2025). QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR. IEEE Transactions on Vehicular Technology.
MLA	Li，Yang,et al."QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR". IEEE Transactions on Vehicular Technology (2025).