Since the inception of network coding in information theory, we have witnessed a sharp increase of research interest in its applications in communications and networking, where the focus has been on more practical aspects. However, thus far, network coding has not been deployed in real-world commercial systems in operation at a large scale, and in a production setting. In this paper, we present the objectives, rationale, and design in the first production deployment of random network coding, where it has been used in the past year as the cornerstone of a large-scale production on-demand streaming system, operated by UUSee Inc., delivering thousands of on-demand video channels to millions of unique visitors each month. To achieve a thorough understanding of the performance of network coding, we have collected 200 Gigabytes worth of real-world traces throughout the 17-day Summer Olympic Games in August 2008, and present our lessons learned after an in-depth trace-driven analysis.

Peer-assisted on-demand video streaming services are extremely large-scale distributed systems on the Internet. Automated demand forecast and performance prediction, if implemented, can help with capacity planning and quality control so that sufficient server bandwidth can always be supplied to each video channel without incurring wastage. In this paper, we use time-series analysis techniques to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements. The proposed mechanisms are evaluated on a large dataset collected from a commercial Internet video-on-demand system.

Abstract. In large-scale P2P live streaming systems, it is shown that peers in an unpopular channel often experience worse streaming quality than those in popular channels. In this paper, by analyzing 130 GB worth of traces from a large-scale P2P streaming system, UUSee, we observe that a large number of "unpopular" channels, those with dozens or hundreds of concurrent peers, tend to experience inferior streaming quality. We also notice a short lifespan in these channels, which further exacerbates streaming quality. To derive useful insights towards improving streaming performance, we seek to thoroughly characterize important factors that may cause peer volatility in unpopular channels. Specifically, we conduct a comprehensive statistical analysis on the impact of various factors on peer lifespan, using survival analysis techniques. We found that the initial buffering level, the variance of peer indegree, and the peer joining time all have important effects on the lifespan of peers.

Multi-touch mobile devices (e.g. iPhone and iPad) and motion-sensing game controllers (e.g. Kinect for Xbox 360) share one common feature: users interact with computing devices in non-conventional gesture-intensive ways, be they multi-touch gestures on the iPad or body motion gestures with the Kinect. As a new way to interact with computing devices, gestures have been proven to be intuitive and natural, with very minimal learning curve. They can be used in applications beyond games, such as those that allow the creation of artistic and musical content in a collaborative fashion. In order for multiple users to collaborate or compete in real time, however, such gestures need to be streamed in multiple broadcast sessions with an "all-to-all" broadcast nature, with each session corresponding to one of users as a source of a gesture stream. These streams of gestures typically incur low yet bursty bit rates, but have unique requirements with respect to delay and loss. In this paper, we present the design of GestureFlow, a gesture broadcast protocol designed specifically for concurrent gesture streams in multiple broadcast sessions. We motivate the effectiveness and practicality of using inter-session network coding, and address challenges introduced by linear dependence, discovered in our extensive experiments involving a new gesture-intensive iPad application that we developed from scratch.

On the basis of the Prandtl boundary layer theory and an improved perturbation method, the process of laminar flow bifurcating into the Southwest China vortex (SWV) in the Hengduan Mountains is studied. The results show that the formation of SWV is mainly determined by the speed of incoming airflow in the direction of the main axis of the Hengduan Mountains. The vortex is generated in the leeward area of the HengduanMountains when the speed of incoming airflow is greater than the critical velocity. Moreover, it means that the laminar flow bifurcates into a vortex. The formation position of the SWV is mainly determined by the relative position of the incoming airflow in the windward area of the Hengduan Mountains and the main axis of the Hengduan Mountains. The seasonal distribution of SWVs is determined by both the velocity of the incoming airflow and the relative position of the incoming airflow to the main axis of the Hengduan Mountains. These findings are consistent with the SWV observation facts, which not only adequately explain the physical formation mechanisms and processes of SWVs, but also present the formation location and seasonal distribution of SWVs. Meanwhile, a solution from laminar to vortex in circumflow motion is also presented.