Bandwidth smoothing techniques for stored video perform end-end workahead transmission of frames from the video source into the destination client video playback buffer in advance of their display times, and are very effective in reducing the bursty transmission bandwidth requirements of compressed, stored video. This paper addresses online bandwidth smoothing for a growing number of streaming video applications such as newscasts, sportscasts and distance learning, where many clients may be willing to tolerate a playback delay of a few seconds in exchange for a smaller bandwidth requirement. The smoothing can be performed at either the end-point source of the videocast or at special smoothing server(s) (e.g., proxies or gateways) within the network. A key difference with smoothing stored video at the source is that, in the online situation, the smoothing server has limited knowledge of frame sizes and access to only a segment of the video at a time. This is either because the feed is live or because it is streaming past the server. We derive an online smoothing model which incorporates playback delay, client and server buffer sizes, server processing capacity, and frame-size prediction techniques. Our model can accomodate an arbitrary arrival process. Using techniques for smoothing stored video at the source as a starting point, we develop an online, window-based smoothing algorithm for these delay-tolerant applications. Extensive experiments with MPEG-1 and M-JPEG video traces demonstrate that online smoothing significantly reduces the peak rate, coefficient of variation, and effective bandwidth of variable-bit-rate video streams. These reductions can be achieved with modest playback delays of a few seconds and moderate client buffer sizes, and closely approximate the performance of optimal offline smoothing of stored video [1] . In addition, we show that frame-size prediction can offer further reduction in resource requirements, though prediction becomes relatively less important for longer playback delays. However, the ability to predict future frame sizes affects the appropriate division of buffer space between the server and client sites. Our experiments show that the optimal buffer allocation shifts to placing more memory at the server as the server has progressively less information about future frame sizes.
An online smoothing algorithm is provided in REF and it is shown that delaying a video transmission by a constant amount of time can reduce the bandwidth required.
Online Smoothing of Variable-Bit-Rate Streaming Video
Computer Science