학술논문

Online playtime prediction for cognitive video streaming
Document Type
Conference
Source
2015 18th International Conference on Information Fusion (Fusion) Information Fusion (Fusion), 2015 18th International Conference on. :1886-1891 Jul, 2015
Subject
Aerospace
Computing and Processing
Photonics and Electrooptics
Signal Processing and Analysis
Streaming media
Quality of service
Neural networks
Bit rate
Predictive models
Hazards
Video quality of service (QoS)
quality of experience (QoE)
internet video
human factors
mean opinion score (MOS)
video quality metrics
machine learning
neural networks
nearest neighbor classification
survival models
Language
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.