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Smooth DASH adaptation exploiting throughput prediction

Published: 03 October 2016 Publication History

Abstract

We present and evaluate a procedure that exploits throughput prediction to select the DASH video quality sequence (sequence of representations) with the highest average bit rate and the fewest quality switches, while explicitly taking into account throughput and time uncertainty. Experiments with an Android implementation of the proposed procedure show that it can exploit throughput prediction to achieve a high QoE (Quality of Experience) with a small number of video pauses and video quality switches, even when the throughput and time uncertainty is large.

References

[1]
A. Bokani, M. Hassan, and S. Kanhere. HTTP-based Adaptive Streaming for Mobile Clients using Markov Decision Process. In Proc. of Int'l Packet Video Workshop, 2013.
[2]
A. Devlic, P. Kamaraju, P. Lungaro, Z. Segall, and K. Tollmar. QoE-aware optimization for video delivery and storage. In Proc. of IEEE WoWMoM, 2015.
[3]
D. Dimopoulos, C. Boursinos, and V. A. Siris. Multi-Source Mobile Video Streaming: Load Balancing, Fault Tolerance, and Offloading with Prefetching. In 9th Int'l Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (Tridentcom), 2014.
[4]
K. Evensen, A. Petlund, H. Riiser, P. Vigmostad, D. Kaspar, C. Griwodz, and P. Halvorsen. Mobile Video Streaming Using Location-Based Network Prediction and Transparent Handover. In Proc. of ACM NOSDAV, 2011.
[5]
T. Hossfeld, M. Seufert, C. Sieber, T. Zinner, and P. Tran-Gia. Identifying QoE optimal adaptation of HTTP adaptive streaming based on subjective studies. Computer Networks, 81:320 -- 332, 2015.
[6]
D. Jarnikov and T. Ozcelebi. Client Intelligence for Adaptive Streaming Solutions. Signal Processing: Image Communication, 26(7):378--389, 2011.
[7]
K. Miller, S. Argyropoulos, N. Corda, A. Raake, and A. Wolisz. Optimal adaptation trajectories for block-request adaptive video streaming. In Proc. of Packet Video Workshop, 2013.
[8]
R. K. P. Mok, X. Luo, E. W. W. Chan, and R. K. C. Chang. QDASH: A QoE-aware DASH System. In Proc. of Multimedia Systems Conference (MMSys), 2012.
[9]
A. J. Nicholson and B. D. Noble. BreadCrumbs: Forecasting Mobile Connectivity. In Proc. of ACM MobiCom, 2008.
[10]
S. Petrangeli, J. Famaey, M. Claeys, S. Latré, and F. De Turck. QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming. ACM Trans. Multimedia Comput. Commun. Appl., 12(2):28:1--28:24, Oct. 2015.
[11]
M. Pinson and S. Wolf. A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3):312--322, Sept 2004.
[12]
D. Z. Rodriguez, Z. Wang, R. L. Rosa, and G. Bressan. The impact of video-quality-level switching on user quality of experience in dynamic adaptive streaming over HTTP. EURASIP Journal on Wireless Communications and Networking, (216), 2014.
[13]
V. Singh, J. Ott, and I. Curcio. Predictive Buffering for Streaming Video in 3G Networks. In Proc. of IEEE WoWMoM, 2012.
[14]
V. A. Siris and D. Dimopoulos. Multi-Source Mobile Video Streaming with Proactive Caching and D2D Communication. In Proc. of Workshop on Video Everywhere, co-located with IEEE WoWMoM, 2015.
[15]
G. Tian and Y. Liu. Towards Agile and Smooth Video Adaptation in Dynamic HTTP Streaming. In Proc. of ACM CoNEXT, 2012.
[16]
J. Yao, S. S. Kahnere, and M. Hassan. Quality Improvement of Mobile Video Using Geo-intelligent Rate Adaptation. In Proc. of IEEE WCNC, 2010.
[17]
X. K. Zou, J. Erman, V. Gopalakrishnan, E. Halepovic, R. Jana, X. Jin, J. Rexford, and R. K. Sinha. Can accurate predictions improve video streaming in cellular networks? In Proc. of HotMobile, 2015.

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cover image ACM Other conferences
MobiArch '16: Proceedings of the Workshop on Mobility in the Evolving Internet Architecture
October 2016
43 pages
ISBN:9781450342575
DOI:10.1145/2980137
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 03 October 2016

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  1. DASH adaptation
  2. throughput prediction
  3. video streaming

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MobiCom'16

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MobiArch '16 Paper Acceptance Rate 6 of 12 submissions, 50%;
Overall Acceptance Rate 47 of 92 submissions, 51%

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