Subjective quality assessment of an adaptive video streaming model

Samira Tavakoli; Kjell Brunnström; Kun Wang; Börje Andrén; Muhammad Shahid; Narciso Garcia

doi:10.1117/12.2040131

3 February 2014 Subjective quality assessment of an adaptive video streaming model

Samira Tavakoli, Kjell Brunnström, Kun Wang, Börje Andrén, Muhammad Shahid, Narciso Garcia

Proceedings Volume 9016, Image Quality and System Performance XI; 90160K (2014) https://doi.org/10.1117/12.2040131
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients’ behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

Citation Download Citation

Samira Tavakoli, Kjell Brunnström, Kun Wang, Börje Andrén, Muhammad Shahid, and Narciso Garcia "Subjective quality assessment of an adaptive video streaming model", Proc. SPIE 9016, Image Quality and System Performance XI, 90160K (3 February 2014); https://doi.org/10.1117/12.2040131

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