An analysis of user behavior in online video streaming

Abstract

Understanding user behavior in online video streaming is essential to designing streaming systems which provide user-oriented service. However, it is challenging to gain insightful knowledge of the characteristics of user behavior due to its high volatility. To this end, the paper provides an extensive analysis of user behavior in online video streaming, based on a large scale trace database of online streaming video access sessions. We categorize user behaviors into multiple patterns and probe the relationship between them. Our work puts emphasis on the statistical characteristics of user behavior patterns. Particularly, this study uncovers that the behavior of one individual user in a video streaming session is not only related to the popularity level of the video, but also has strong correlation with the user's behaviors in previous streaming sessions.