Bandwidth consumption is a significant concern for online video service providers. Practical video streaming systems usually use some form of HTTP streaming (progressive download) to let users download the video at a faster rate than the video bitrate. Since users may quit before viewing the complete video, however, much of the downloaded video will be "wasted". To the extent that users' departure behavior can be predicted, we develop smart streaming that can be used to improve user QoE with limited server bandwidth or save bandwidth cost with unlimited server bandwidth. Through measurement, we extract certain user behavior properties for implementing such smart streaming, and demonstrate its advantage using prototype implementation as well as simulations.
@cite_4: Video-on-demand over IP (VOD) is one of the best-known examples of "next-generation" Internet applications cited as a goal by networking and multimedia researchers. Without empirical data, researchers have generally relied on simulated models to drive their design and developmental efforts. In this paper, we present one of the first measurement studies of a large VOD system, using data covering 219 days and more than 150,000 users in a VOD system deployed by China Telecom. Our study focuses on user behavior, content access patterns, and their implications on the design of multimedia streaming systems. Our results also show that when used to model the user-arrival rate, the traditional Poisson model is conservative and overestimates the probability of large arrival groups. We introduce a modified Poisson distribution that more accurately models our observations. We also observe a surprising result, that video session lengths has a weak inverse correlation with the video's popularity. Finally, we gain better understanding of the sources of video popularity through analysis of a number of internal and external factors.
@cite_8: In this paper, we present a study on video viewing behavior. Based on a well-suited Markovian model, we have developed a clustering algorithm called K-models and inspired by the K-means technique to cluster and analyze behaviors. These models are constructed using the different actions proposed to the user while he is viewing a video sequence (play, pause, forward, rewind, jump, stop). We have applied our algorithm with a movie trailer mining tool. This tool allows users to perform searches on basic attributes (cast, director, onscreen date...) and to watch selected trailers. With an appropriate server, we log every action to analyze behaviors. First results obtained from a set of beta users reveal interesting typical behaviors.
@cite_21: In this paper we present a complete measurement study that compares YouTube traffic generated by mobile devices (smart-phones,tablets) with traffic generated by common PCs (desktops, notebooks, netbooks). We investigate the users' behavior and correlate it with the system performance. Our measurements are performed using unique data sets which are collected from vantage points in nation-wide ISPs and University campuses from two countries in Europe and the U.S. Our results show that the user access patterns are similar across a wide range of user locations, access technologies and user devices. Users stick with default player configurations, e.g., not changing video resolution or rarely enabling full screen playback. Furthermore it is very common that users abort video playback, with 60 of videos watched for no more than 20 of their duration. We show that the YouTube system is highly optimized for PC access and leverages aggressive buffering policies to guarantee excellent video playback. This however causes 25 -39 of data to be unnecessarily transferred, since users abort the playback very early. This waste of data transferred is even higher when mobile devices are considered. The limited storage offered by those devices makes the video download more complicated and overall less efficient, so that clients typically download more data than the actual video size. Overall, this result calls for better system optimization for both, PC and mobile accesses.
@cite_25: 
@cite_17: 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.
The early departure behavior has been considered in some previous works. For example, it was studied as a activity for users to shop for videos they like @cite_25 , based on data collected on a campus network. Another work @cite_21 reported statistics of video playback aborts - 60 Other works studied access patterns by users. For example, user arrival patterns are analyzed and modeled in @cite_4 . Some works focus on the transition probability of different user behaviors. In @cite_8 , the @math -means technique is used to retrieve and cluster user behaviors using a Markovian model based on a movie trailer database. Another work @cite_17 probes the relationship between several types of user behavior and uncovers that the behavior of one individual user in a video streaming session has strong correlation with the user's behaviors in previous streaming sessions. While these works tell us more about user behavior, their results are not particularly helpful in designing our rate allocation algorithms.