Online playtime prediction for cognitive video streaming

Author

Pasupuleti, D. ; Mannaru, P. ; Balasingam, B. ; Baum, M. ; Pattipati, K. ; Willett, P. ; Lintz, C. ; Commeau, G. ; Dorigo, F. ; Fahrny, J.

Author_Institution

University of Connecticut, CT, USA

fYear

2015

fDate

6-9 July 2015

Firstpage

1886

Lastpage

1891

Abstract

In this paper, we consider the problem of cognitive video streaming in video on demand (VoD) services. The focus lies on quantities that are indicative of the quality of experience (QoE) of the subscriber, such as playtime ratio, probability of return, probability of replay and startup time. Especially, in this paper, we develop and evaluate a playtime prediction tool. For this purpose, the applicability of different machine learning algorithms such as k-nearest neighbor, neural network regression, and survival models is investigated; then, we develop an approach to identify the most relevant factors that contributed to the prediction. The proposed approaches are tested by means of a data set provided by Comcast.

Keywords

Bit rate; Hazards; Neural networks; Predictive models; Quality of service; Streaming media; Video quality of service (QoS); human factors; internet video; machine learning; mean opinion score (MOS); nearest neighbor classification; neural networks; quality of experience (QoE); survival models; video quality metrics;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (Fusion), 2015 18th International Conference on

Conference_Location

Washington, DC, USA

Type

conf

Filename

7266785