Video popularity (measured by view count) over time is an essential reference for both online video providers and users. According to state-of-the-art works, video popularity is useful for system optimization, load generation, video caching, and video recommendation. Thus, deeper understanding of video popularity evolution is very helpful for improving video service quality and providers' operating efficiency. The core question to be explored in this paper is what key factors govern online video popularity evolution? Through collaboration with our industry partner, Tencent Video, we obtain historical data of video view counts over a period of time, and observe their patterns. We then propose a stochastic fluid model, named as EvoModel, which captures two processes giving rise to different evolution patterns of a given video: (a) the information spreading process and (b) the user reaction process. The driving forces for process (a) can be either via recommendation from the system directly, or word-of-mouth; the extent of the spread is governed by the intrinsic popularity of the video. The factor affecting the second process can be modeled by a user reaction rate. These processes together determine different video popularity evolution patterns. We validate our model by fitting the historical data obtained from a real-world system. Furthermore, we discuss the feasibility of estimating model parameters and predicting popularity.

Large-scale Internet VoD services often adopt a hybrid overlay approach using both P2P and CDN to stream video content. When the underlay ISP network interconnection is weak, as the situation is found in China, it is important to provision the CDN bandwidth carefully in different ISP networks. Through measurement studies using real world data (from Tencent Video), it is found that users in large ISP networks can leverage more on P2P service, compared to the case for smaller ISP networks, and this is especially true during heavy load periods. We study systematic strategies for allocating CDN bandwidth given fixed total bandwidth purchasing budget, and the influence of P2P factor. We show that a more optimal strategy would bias towards allocating proportionally more CDN bandwidth to smaller ISP networks. This result from our model and analysis is validated by simulation. This problem has not been considered by past works and has practical implications for improving the operation of real-world VoD service providers.

Large Internet video delivery systems serve millions of videos to tens of millions of users on daily basis, via Video-on-Demand (VoD) and live streaming. Video popularity (measured by view count) evolves over time. It represents the workload, as well as business value, of the video to the overall system. The ability to predict video popularity is very helpful for improving service quality and operating efficiency. Previous studies adopted simple (usually static) models for video popularity, or directly adopted patterns from measurement studies. In this paper, we develop a fluid model that tries to capture two hidden processes that give rise to different patterns of a given video's popularity evolution: (a) the information spreading process, and (b) the user reaction process. Specifically, these processes model how the video is recommended to the users, the video's inherent attractiveness, and users' reaction rate; and yield different popularity evolution patterns. We validate our model by fitting the data obtained from a large content provider in China. This model gives us the insight to explain the common and different video popularity evolution patterns and why.

Rapid growth in users of smartphones and other mobile devices is increasing the demand for online video service to these mobile devices. The mobile video on demand (VoD) service is almost exclusively provided by content delivery network (CDN) servers since mobile devices are not powerful enough to provide any peer-to-peer (P2P) service. However, in a mixed VoD system with both non-mobile and mobile peers, an interesting question is to ask whether we can leverage non-mobile peers to provide better support for VoD. In this paper, we try to answer this question by a measurement study on Tencent's existing VoD systems. Our measurement results show various interesting characteristics and potential benefits of leveraging non-mobile users. Such results will guide us towards a practical system design and implementation in the near future.