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Improve Adaptive Video Streaming through Machine Learning

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  • Fri, 01/19/2018 - 10:00am - 12:00pm




 

Improve Adaptive Video Streaming through Machine Learning

MSc Thesis Defense by:

Anh Minh Le

Date:  Friday, January 19, 2018
Time:  10:00 am – 12:00pm
Location: 3105, Lambton Tower

Abstract: Many Adaptive Bitrate (ABR) algorithms have emerged recently in order to improve the Quality of Experience (QoE) in video streaming. These ABRs all function by predicting the future throughput rate of the current video session. Such prediction could be formulated into the parameters to the algorithms. We follow the data-driven approach to learning the best parameter values from the logged sessions of throughput values. To improve the quality of the prediction, we propose to properly classify the logged sessions according to the critical features that affect the network conditions such as Internet Service Provider(ISP), geographical location etc. In this thesis, we present our work on modifying the existing Decision Tree algorithm for our feature-based session partitioning to help learning the best parameter values and improve the performance of existing ABR algorithms. The experiment shows that this approach can improve the performance of the ABR algorithm by up to 8.59%, with 98.38% of the testing sessions performing better.

Thesis Committee:
Internal Reader:          Dr. Stephanos Mavromoustakos
External Reader:         Dr. Sévérien Nkurunziza
Advisor:                      Dr. Jessica Chen
Chair:                          Dr. Yung H. Tsin



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