Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using scalable video coding (SVC) scheme over highly variable bandwidth conditions, such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel quality-of-experience metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop layered bin packing (LBP) adaptation algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed, including handling bandwidth prediction errors and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared with the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.
Bibliographical noteFunding Information:
Manuscript received October 22, 2017; revised April 5, 2018; accepted May 22, 2018; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor T. Hou. Date of publication June 22, 2018; date of current version August 16, 2018. The work of A. Elgabli and V. Aggarwal was supported by the U.S. National Science Foundation under Grants CCF-1527486 and CNS-1618335. (Corresponding author: Vaneet Aggarwal.) A. Elgabli is with the School of Electrical and Computer Engineering, Pur-due University, West Lafayette, IN 47907 USA (e-mail: email@example.com). V. Aggarwal is with the School of Industrial Engineering, Purdue University, West Lafayette, IN 47907 USA (e-mail: firstname.lastname@example.org). S. Hao and S. Sen are with AT&T Labs Research, Bedminster, NJ 07921 USA (e-mail: email@example.com; firstname.lastname@example.org). F. Qian is with the Department of Computer Science, Indiana University Bloomington, Bloomington, IN 47405 USA (e-mail: email@example.com). This paper has supplementary downloadable material available at http://ieeexplore.ieee.org, provided by the authors. Digital Object Identifier 10.1109/TNET.2018.2844123
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- Video streaming
- adaptive bit rate streaming
- bandwidth prediction
- combinatorial optimization
- scalable video coding