Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding

doi:10.1109/TMC.2018.2889039

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding
	Elgabli, Anis 2; Liu, Ke 1,3; Aggarwal, Vaneet 3
刊名	IEEE TRANSACTIONS ON MOBILE COMPUTING
	2020
卷号	19 期号:1 页码:159-172
关键词	Static VAr compensators Streaming media Prediction algorithms Bandwidth Video coding Quality of experience Optimization Video streaming multi-path scalable video coding video quality stall duration multi-path TCP non convex optimization
ISSN号	1536-1233
DOI	10.1109/TMC.2018.2889039
英文摘要	Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users' quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The proposed metric prefers to use bandwidth of the links to optimize a concave utility function of the chunk quality. Even though the formulation is a non-convex discrete optimization, we provide a quadratic complexity algorithm which is shown to be optimal in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.
资助项目	US National Science Foundation[CCF-1527486] ; US National Science Foundation[CNS-1618335] ; National Natural Science Foundation of China (NSFC)[61502459]
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000502276200012
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/14984]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Aggarwal, Vaneet
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Oulu, Ctr Wireless Commun, Oulu 90014, Finland 3.Purdue Univ, Sch Ind Engn, W Lafayette, IN 47907 USA
推荐引用方式 GB/T 7714	Elgabli, Anis,Liu, Ke,Aggarwal, Vaneet. Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2020,19(1):159-172.
APA	Elgabli, Anis,Liu, Ke,&Aggarwal, Vaneet.(2020).Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding.IEEE TRANSACTIONS ON MOBILE COMPUTING,19(1),159-172.
MLA	Elgabli, Anis,et al."Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding".IEEE TRANSACTIONS ON MOBILE COMPUTING 19.1(2020):159-172.