QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks

doi:10.1109/TMC.2021.3133949

	QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks
	Liu, Xuewen 1; Chuai, Gang 1; Wang, Xin 2; Xu, Zhiwei 2,3; Gao, Weidong 1
	2023-06-01
发表期刊	IEEE TRANSACTIONS ON MOBILE COMPUTING
ISSN	1536-1233
卷号	22 期号:6 页码:3619-3633
摘要	Quality of experience (QoE) is a vital metric that indicates how well the wireless network provides transmission services to users, while quality of service (QoS) help better configure the network parameters for higher performance. The evaluation time of QoE is usually several orders of magnitude larger than that of QoS, because QoE is the perception of users over a period of time, but QoS can be collected every millisecond. Therefore, the implementation of QoE/QoS mapping model can help us obtain QoE by collecting the QoS measurements, and perform QoE-based network configurations with smaller time granularity. Many studies are made to obtain the QoS to QoE mapping, including the use of machine learning (ML) methods. However, traditional ML-based regression methods for QoE/QoS mapping face the challenge of high regression error and catastrophic forgetting in dealing with continuously arriving data. In this paper, we propose a novel QoE model based on continual deep learning in wireless network. This model is formed with two deep neural networks (DNNs) concatenated. The first DNN classifies data into different subsets, which are then fed into the second DNN for regression. The second DNN dynamically form the corresponding subnets, each with nodes and connections adaptively selected in each new time period with new arriving data. We solve the catastrophic forgetting problem with the use of node splitting and hidden state augmentation. Our proposed learning framework greatly reduces the regression error to as low as 0.9314%. The experimental results demonstrate that our proposed model reduces the root mean square error (RMSE) by 21 similar to 86 times compared with several existing approaches, specially, the testing error of our proposed model is more than 80 times lower than that of traditional DNN. Compared with other DNN-based cascade models, our proposed method provides good performance in both training time and RMSE.
关键词	Quality of experience Quality of service Training Data models Wireless networks Computational modeling Mobile computing Data-driven QoE assessment continual deep learning QoE QoS mapping wireless network cascaded DNNs
DOI	10.1109/TMC.2021.3133949
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Project of China[2020YFB1806703] ; National Science Foundation[ECCS 2030063] ; China Scholarship Council
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000982912400014
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21184
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chuai, Gang
作者单位	1.Beijing Univ Posts & Telecommun, Dept Informat & Commun Engn, Beijing 100876, Peoples R China 2.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA 3.Chinese Acad Sci, Inst Comp, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xuewen,Chuai, Gang,Wang, Xin,et al. QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2023,22(6):3619-3633.
APA	Liu, Xuewen,Chuai, Gang,Wang, Xin,Xu, Zhiwei,&Gao, Weidong.(2023).QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks.IEEE TRANSACTIONS ON MOBILE COMPUTING,22(6),3619-3633.
MLA	Liu, Xuewen,et al."QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks".IEEE TRANSACTIONS ON MOBILE COMPUTING 22.6(2023):3619-3633.