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Improving Adaptive Video Streaming Through Learning

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  • Fri, 09/22/2017 - 10:00am - 12:00pm

Improving Adaptive Video Streaming through Learning

MSc Thesis Proposal by:

Anh Minh Le

Date:  Friday, September 22nd, 2017
Time:  10: 00 am – 12:00 pm
Location: 3105, Lambton Tower

Abstract: While the internet video is gaining increasing popularity and soaring to dominate the network traffic, extensive study is being carried out on how to achieve higher Quality of Experience (QoE) in its content delivery. Associated with the HTTP chunk-based streaming protocol, the Adaptive Bitrate (ABR) algorithms have recently emerged to cope with the diverse and fluctuating network conditions by dynamically adjusting bitrates for future chunks. This inevitably involves predicting the future throughput of a video session. Predicting parameters being part of the ABR design, we propose to follow the data-driven approach to learn the best setting of these parameters from the study of the backlogged throughput traces of previous video sessions. To further improved the quality of the prediction, we propose to follow the Decision Tree approach to properly classify the logged sessions according to those critical features that affect the network conditions, e.g. Internet Service Provider (ISP), geographical location etc. Given that the splitting criterion will have to be defined together with the selection among target variables, existing Decision Tree solutions cannot be directly applied. In this thesis, some existing Decision Tree algorithm will be properly tailored to help learning the best parameter values and the performance of the ABRs with the learnt parameter values will be evaluated in comparison with the existing results in the literature.

Thesis Committee:
Internal Reader: Dr. Stephanos Mavromoustakos
External Reader: Dr. Sévérien Nkurunziza
Advisor: Dr. Jessica Chen

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