Previous studies of peer-to-peer (P2P) video-on-demand (VoD) are performed separately from studies utilizing adaptive bit rate video since the techniques seemingly tackle orthogonal goals. Additionally, previous policies used by P2P VoD do not account for viewer abandonment of video during download and playback. Through analysis, we show that the popularity of a P2P swarm and seed staying time ...

What makes content go viral? Which videos become popular and why others don’t? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these th...

Rarely do users watch online contents entirely. We study how to take this into account to improve the performance of cache systems for video-on-demand and video-sharing platforms in terms of traffic reduction on the core network. We exploit the notion of “Audience retention rate”, introduced by mainstream online content platforms and measuring the popularity of different parts of the same video...

Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is important for the sustainable deployment of content distribution systems. A significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, little wo...

Unified video-on-demand (UVoD) is a recently proposed architecture that integrates multicast transmission with unicast transmission to improve system efficiency. Streaming channels in a UVoD system are divided into unicast and multicast channels, with the multicast channels further divided equally among all videos. This uniform channel-allocation scheme is simple to design and implement, but th...

YouTube offers a great opportunity for people to entertain, advertise, gain popularity, and generate revenue. How to increase views for a video has become the key question for anyone who wish to be famous or gain more revenue. Recognizing that a recommendation system is a major view source for videos, our goal in this paper is to increase video views in YouTube by leveraging on the recommendati...

Many multimedia applications rely on video streaming techniques. However, large scale video delivery is still very challenging since it requires a large amount of resources such as storage space, network bandwidth and I/O bandwidth. In this paper we propose a proxy-assisted periodic broadcast architecture for video delivery to a large number of clients over the Internet. Our video delivery tech...

Video sharing sites, such as YouTube, use video responses to enhance the social interactions among their users. The video response feature allows users to interact and converse through video, by creating a video sequence that begins with an opening video and followed by video responses from other users. Our characterization is over 3.4 million videos and 400,000 video responses collected from Y...

In this paper, we address the problem of popularity prediction of online videos shared in social media. We prove that this challenging task can be approached using recently proposed deep neural network architectures. We cast the popularity prediction problem as a classification task and we aim to solve it using only visual cues extracted from videos. To that end, we propose a new method based o...