Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter

doi:10.1145/3672399

	Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter
	Yu, Encheng 1,2; Zhou, Jianer 2; Li, Zhenyu 3; Tyson, Gareth 4; Li, Weicho 2; Zhang, Xinyi 5; Xu, Zhiwei 6; Xie, Gaogang 5
	2024-09-01
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
卷号	20 期号:9 页码:23
摘要	The advent of 5G and interactive live broadcasting has led to a growing trend of people preferring realtime interactive video services on mobile devices, particularly mobile phones. In this work, we measure the performance of Google congestion control (GCC) in cellular networks, which is the default congestion control algorithm for Web Real-Time Communication (WebRTC). Our measurements show that GCC sometimes makes bitrate decisions which are harmful to quality of experience (QoE) in cellular networks with high jitter. We further find that the frame delivery time (FDT) in the player can mitigate network jitter and maintain QoE. Moreover, the receiving rate is better to reflect the network congestion than RTT in cellular networks. Based on these measurements and findings, we propose Mustang, an algorithm designed to overcome the jitter in cellular networks. Mustang makes use of the FDT and receiving rate as feedback information to the sender. Then the sender adjusts its sending rate based on the information to guarantee QoE. We have implemented Mustang in WebRTC and evaluated it in both emulated and real cellular networks. The experimental results show that Mustang can improve WebRTC's QoS and QoE performance. For QoS, Mustang increases the sending rate by 72.1% and has similar RTT and packet loss when compared with GCC, while it is about 30% better for QoE.
关键词	Information systems Multimedia streaming Networks Transport protocols Transport protocols
DOI	10.1145/3672399
收录类别	SCI
语种	英语
资助项目	Major Key Project of PCL[PCL2023AS1-5] ; Major Key Project of PCL[PCL2023AS1-3] ; Young Scientists Fund of the National Natural Science Foundation of China[62202447]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001325878000002
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39552
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yu, Encheng
作者单位	1.Southern Univ Sci & Technol, Shenzhen, Peoples R China 2.Peng Cheng Lab, Shenzhen, Peoples R China 3.Chinese Acad Sci, ICT, Beijing, Peoples R China 4.Hong Kong Univ Sci & Technol, Guangzhou, Peoples R China 5.Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China 6.Haihe Lab Informat Technol & Applicat Innovat, Tianjin, Peoples R China
推荐引用方式 GB/T 7714	Yu, Encheng,Zhou, Jianer,Li, Zhenyu,et al. Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2024,20(9):23.
APA	Yu, Encheng.,Zhou, Jianer.,Li, Zhenyu.,Tyson, Gareth.,Li, Weicho.,...&Xie, Gaogang.(2024).Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,20(9),23.
MLA	Yu, Encheng,et al."Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 20.9(2024):23.