Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.Comment: 7 pages, 15 figure

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects

Predictive Buffering for Streaming Video in 3G Networks

Abstract—This paper presents a multimedia streaming service in a mobile (3G) environment that, in addition to in-band congestion signals such as packet losses and delay variations, receives congestion cues from a Network Coverage Map Service (NCMS) to make rate-control decisions. The streaming client routinely queries the NCMS to assess the network conditions at future locations along its expected path. The streaming client may ask the streaming server for short-term transmission bursts to increase pre-buffering when it is approaching areas with bad network performance to maintain media quality. If needed, the client may also switch to a different encoding rate (rate-switching) depending on the severity of expected congestion. These notifications are scheduled as late as possible, so that any changes in network conditions and/or changes in user’s movements can be taken into account (late scheduling). Using this type of geo-predictive media streaming service we show that the streaming client can provide pause-less playback and better quality of experience to the user