Internet Protocol TV (IPTV) normally has the advantage of providing far more TV channels than the traditional TV services, while as the other side of the coin it has the problem of information overload. Users of IPTV usually have difficulties finding channels matching their interests. In this paper, using a large IPTV dataset, we analyze channel zapping behaviors of IPTV users and discover various patterns that can be used to generate more accurate channel zapping recommendations. Based on user behavior analysis, we develop several base and fusion recommender systems that generate in real-time a short list of channels for users to consider whenever they want to switch channels. A deep neural network model that consists of a “Recommender System Attention (RS Attention)” module and a “Channel Attention” module capturing the static and dynamic user switching behaviors is also developed to further improve the recommendation accuracy. Evaluation on the IPTV dataset demonstrates that our fusion recommender can achieve 41% hit ratio with only three candidate channels, and our attention neural network model further pushes it up to 45%. Our recommender systems only take as input user channel zapping sequences, and can be easily adopted by IPTV systems with low data and computation overheads.

Compared with the traditional television services, Internet Protocol TV (IPTV) can provide far more TV channels to end users. However, it may also make users feel confused even painful to find channels of their interests from a large number of them. In this paper, using a large IPTV trace, we analyze user channel-switching behaviors to understand when, why and how they switch channels. Based on user behavior analysis, we develop several base and fusion recommender systems that generate in real-time a short list of channels for users to consider whenever they want to switch channels. Evaluation on the IPTV trace demonstrates that our recommender systems can achieve up to 45 percent hit ratio with only three candidate channels. Our recommender systems only need access to user channel watching sequences, and can be easily adopted by IPTV systems with low data and computation overheads.

Video telephony is increasingly being adopted by end consumers. It is extremely challenging to deliver video calls over wireless networks. In this paper, we conduct a measurement study on three popular mobile video call applications: Face-Time, Google Plus Hangout, and Skype, over both WiFi and Cellular links. We study the following questions: 1) how they encode/decode video in realtime under tight resource constraints on mobile devices? 2) how they transmit video smoothly in the face of various wireless network impairments? 3) what is their delivered video conferencing quality under different mobile network conditions? 4) how different system architectures and design choices contribute to their delivered quality? Through detailed analysis of measurement results, we obtain valuable insights regarding the unique challenges, advantages and disadvantages of existing design solutions, and possible directions to deliver high-quality video calls in wireless networks.

Video telephony requires high-bandwidth and low-delay voice and video transmissions between geographically distributed users. It is challenging to deliver high-quality video telephony to end-consumers through the best-effort Internet. In this paper, we present our measurement study on three popular video telephony systems on the Internet: Google+, iChat, and Skype. Through a series of carefully designed active and passive measurements, we uncover important information about their key design choices and performance, including application architecture, video generation and adaptation schemes, loss recovery strategies, end-to-end voice and video delays, resilience against random and bursty losses, etc. The obtained insights can be used to guide the design of applications that call for high-bandwidth and low-delay data transmissions under a wide range of “best-effort” network conditions.

Video telephony requires high-bandwidth and low-delay voice and video transmissions between geographically distributed users. It is challenging to deliver high-quality video telephony to end-consumers through the best-effort Internet. In this paper, we present our measurement study on three popular video telephony systems on the Internet: Google+, iChat, and Skype. Through a series of carefully designed active and passive measurements, we are able to unveil important information about their key design choices and performance, including application architecture, video generation and adaptation schemes, loss recovery strategies, end-to-end voice and video delays, resilience against random and bursty losses, etc. Obtained insights can be used to guide the design of applications that call for high-bandwidth and low-delay data transmissions under a wide range of "best-effort" network conditions.