A. Elgabli, K. Liu and V. Aggarwal, "Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding," in IEEE Transactions on Mobile Computing, vol. 19, no. 1, pp. 159-172, 1 Jan. 2020, doi: 10.1109/TMC.2018.2889039
Optimized preference-aware multi-path video streaming with scalable video coding
|Author:||Elgabli, Anis1; Liu, Ke2; Aggarwal, Vaneet3|
1Center of Wireless Communications, University of Oulu, Oulu 90014, Finland
2Institute of Computing Technology, Chinese Academy of Sciences, and with the School of Industrial Engineering, Purdue University, West Lafayette, IN 47907
3School of Industrial Engineering, Purdue University, West Lafayette, IN 47907
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020060841080
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-06-08
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.
IEEE transactions on mobile computing
|Pages:||159 - 172|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the US National Science Foundation under grants CCF-1527486 and CNS-1618335, National Natural Science Foundation of China (NSFC) under Grant No. 61502459. This work was presented in part at SPCOM, Jul. 2018 . A. Elgabli was with Purdue University when thiswork was performed.
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