Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement

Ibrahim Alzahrani; Naeem Ramzan; Stamos Katsigiannis; Abbes Amira

doi:10.1007/978-3-319-78753-4_1

Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement

Ibrahim Alzahrani, Naeem Ramzan, Stamos Katsigiannis, Abbes Amira

School of Computing, Engineering and Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

215 Downloads (Pure)

Abstract

Dynamic adaptive video streaming over HTTP (DASH) has been developed as one of the most suitable technologies for the transmission of live and on-demand audio and video content over any IP network. In this work, we propose a machine learning-based method for selecting the optimal target quality, in terms of bitrate, for video streaming through an MPEG-DASH server. The proposed method takes into consideration both the bandwidth availability and the client’s buffer state, as well as the bitrate of each video segment, in order to choose the best available quality/bitrate. The primary purpose of using machine learning for the adaptation is to let clients know/learn about the environment in a supervised manner. By doing this, the efficiency of the rate adaptation can be improved, thus leading to better requests for video representations. Run-time complexity would be minimized, thus improving QoE. The experimental evaluation of the proposed approach showed that the optimal target quality could be predicted with an accuracy of 79%, demonstrating its potential.

Original language	English
Title of host publication	5th International Symposium on Data Mining Applications (SDMA 2018)
Editors	Mamdouh Alenezi, Basit Qureshi
Publisher	Springer
Pages	3-11
Number of pages	9
ISBN (Electronic)	978-3-319-78753-4
ISBN (Print)	978-3-319-78752-7
DOIs	https://doi.org/10.1007/978-3-319-78753-4_1
Publication status	E-pub ahead of print - 29 Mar 2018
Event	5th International Symposium on Data Mining Applications - Prince Sultan University, Riyadh, Saudi Arabia Duration: 21 Mar 2018 → 22 Mar 2018 http://info.psu.edu.sa/ResearchEvents/SDMA2018/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	753
ISSN (Electronic)	2194-5357

Conference

Conference	5th International Symposium on Data Mining Applications
Abbreviated title	SDMA2018
Country/Territory	Saudi Arabia
City	Riyadh
Period	21/03/18 → 22/03/18
Internet address	http://info.psu.edu.sa/ResearchEvents/SDMA2018/

Access to Document

10.1007/978-3-319-78753-4_1

2018 03 29 Alzahrani et al Use acceptedAccepted author manuscript, 305 KBLicence: CC BY-NC-ND

Cite this

Alzahrani, I., Ramzan, N., Katsigiannis, S., & Amira, A. (2018). Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement. In M. Alenezi, & B. Qureshi (Eds.), 5th International Symposium on Data Mining Applications (SDMA 2018) (pp. 3-11). (Advances in Intelligent Systems and Computing; Vol. 753). Springer. Advance online publication. https://doi.org/10.1007/978-3-319-78753-4_1

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abstract = "Dynamic adaptive video streaming over HTTP (DASH) has been developed as one of the most suitable technologies for the transmission of live and on-demand audio and video content over any IP network. In this work, we propose a machine learning-based method for selecting the optimal target quality, in terms of bitrate, for video streaming through an MPEG-DASH server. The proposed method takes into consideration both the bandwidth availability and the client{\textquoteright}s buffer state, as well as the bitrate of each video segment, in order to choose the best available quality/bitrate. The primary purpose of using machine learning for the adaptation is to let clients know/learn about the environment in a supervised manner. By doing this, the efficiency of the rate adaptation can be improved, thus leading to better requests for video representations. Run-time complexity would be minimized, thus improving QoE. The experimental evaluation of the proposed approach showed that the optimal target quality could be predicted with an accuracy of 79%, demonstrating its potential.",

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Alzahrani, I, Ramzan, N, Katsigiannis, S & Amira, A 2018, Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement. in M Alenezi & B Qureshi (eds), 5th International Symposium on Data Mining Applications (SDMA 2018). Advances in Intelligent Systems and Computing, vol. 753, Springer, pp. 3-11, 5th International Symposium on Data Mining Applications, Riyadh, Saudi Arabia, 21/03/18. https://doi.org/10.1007/978-3-319-78753-4_1

Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement. / Alzahrani, Ibrahim; Ramzan, Naeem; Katsigiannis, Stamos et al.
5th International Symposium on Data Mining Applications (SDMA 2018). ed. / Mamdouh Alenezi; Basit Qureshi. Springer, 2018. p. 3-11 (Advances in Intelligent Systems and Computing; Vol. 753).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement

AU - Alzahrani, Ibrahim

AU - Ramzan, Naeem

AU - Katsigiannis, Stamos

AU - Amira, Abbes

PY - 2018/3/29

Y1 - 2018/3/29

N2 - Dynamic adaptive video streaming over HTTP (DASH) has been developed as one of the most suitable technologies for the transmission of live and on-demand audio and video content over any IP network. In this work, we propose a machine learning-based method for selecting the optimal target quality, in terms of bitrate, for video streaming through an MPEG-DASH server. The proposed method takes into consideration both the bandwidth availability and the client’s buffer state, as well as the bitrate of each video segment, in order to choose the best available quality/bitrate. The primary purpose of using machine learning for the adaptation is to let clients know/learn about the environment in a supervised manner. By doing this, the efficiency of the rate adaptation can be improved, thus leading to better requests for video representations. Run-time complexity would be minimized, thus improving QoE. The experimental evaluation of the proposed approach showed that the optimal target quality could be predicted with an accuracy of 79%, demonstrating its potential.

AB - Dynamic adaptive video streaming over HTTP (DASH) has been developed as one of the most suitable technologies for the transmission of live and on-demand audio and video content over any IP network. In this work, we propose a machine learning-based method for selecting the optimal target quality, in terms of bitrate, for video streaming through an MPEG-DASH server. The proposed method takes into consideration both the bandwidth availability and the client’s buffer state, as well as the bitrate of each video segment, in order to choose the best available quality/bitrate. The primary purpose of using machine learning for the adaptation is to let clients know/learn about the environment in a supervised manner. By doing this, the efficiency of the rate adaptation can be improved, thus leading to better requests for video representations. Run-time complexity would be minimized, thus improving QoE. The experimental evaluation of the proposed approach showed that the optimal target quality could be predicted with an accuracy of 79%, demonstrating its potential.

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Alzahrani I, Ramzan N, Katsigiannis S, Amira A. Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement. In Alenezi M, Qureshi B, editors, 5th International Symposium on Data Mining Applications (SDMA 2018). Springer. 2018. p. 3-11. (Advances in Intelligent Systems and Computing). Epub 2018 Mar 29. doi: 10.1007/978-3-319-78753-4_1

Use of machine learning for rate adaptation in MPEG-DASH for quality of experience improvement

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