Abstract
The expansion of the online video content continues in every area of the modern connected world and the need for measuring and predicting the Quality of Experience (QoE) for online video systems has never been this important. This paper has designed and developed a machine learning based methodology to derive QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to users so that objective video metrics are collected into a database. At the end of each video session, users are queried with a subjective survey about their experience. Both quantitative statistics and qualitative user survey information are used as training data to a variety of machine learning techniques including Artificial Neural Network (ANN), K-nearest Neighbours Algorithm (KNN) and Support Vector Machine (SVM) with a collection of cross-validation strategies. This methodology can efficiently answer the problem of predicting user experience for any online video service provider, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative system capacity metrics.
Highlights
Over the last decade, video has become the main component of the Web
This paper provides a better understanding of user Quality of Experience (QoE) regarding a wide variety of video metrics including total stall duration, number of stalls, initial buffering and resolution at the same time through machine learning modelling
The collected data have been used for modelling with K-nearest Neighbours Algorithm (KNN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) on a MacBook Pro running Matlab R17 with i5 processor and 16 GB RAM
Summary
Video has become the main component of the Web. In today’s world, social media, news channels, conventional television broadcasting and most of telephony products are all built upon video services [1, 2, 42]. Multimedia Tools and Applications (2019) 78:18787–18811 content provider company fails to deliver the content in expected time and quality, the user might tend to cancel their subscription regardless if it is a paid or a free service. In an ideal world, where each user sends information about their experience, it would be easy to translate this instant feedback from user’s feelings into system and network parameters to increase customer satisfaction. Only a very small percentage of the consumers provide instant feedback about the service experience. This information can be translated into valuable feedback as many of the frontrunner companies like Facebook, Whatsapp and Skype frequently employ these methodologies
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