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Project Description
The current best-effort Internet does not guarantee the bandwidth availability between a receiver and a sender, and so
renders any quality-of-service (QoS) control difficult, if not impossible. This research project investigates reactive and predictive approaches to providing QoS control in streaming video over the best-effort Internet.
Reactive approaches refer to algorithms that monitors the network resources availability and then perform content
adaptation or delivery routing to adapt to variations in the resources available. For example, this project developed a new adaptation algorithm to adjust the bit-rate of video data [1] in response to the network
bandwidth available to improve playback continuity. Unlike previous works, the proposed algorithm requires no parameter tuning and yet can achieve better performance in most cases [2]. In addition to performing
adaptation within a video stream, we also investigate the feasibility to perform bandwidth reallocation across multiple video streams in a TCP-friendly manner. The first step is to develop algorithms to reallocat
bandwidth across a group of TCP flows sharing the same network bottleneck [3], and then the second step will be to develop data and bandwidth allocation algorithms to dynamically adjust the assigned bandwidth to
improve QoS.
Predictive approaches refer to algorithms that not only react to observed changes in network resources availability, but
also estimate and predict their future availability to support admission control, buffering, or even combined with adaptation. In particular, with the rapid growth of peer-to-peer applications, more and more
applications will generate many-to-one rather than one-to-one traffic flows. Our investigation revealed that the aggregate bandwidth from multiple senders, while still varies randomly, is far more predictable than a
single sender. Thus this increased predictability could open a new way to achieve probabilistic performance guarantees in multimedia applications.
The early measurement experiments conducted in the PlanetLab and in the Internet have
been very promising [4-5]. Using these measurement data in trace-driven simulations we found that the statistical properties of many-to-one data flows can be effectively
exploited for playback adaptation [6] as well as prefetch buffering [7-8].
Publication
Peter H. W. Wong, Robert T. W. Hung, Jack Y. B. Lee, S. C. Liew, C. S. Kim, and Roland T. Chin, "Rate Estimation For H.264/AVC Spatial Resolution Reduction," Proc. IEEE International Conference on Image Processing (ICIP 2004), Oct 24-27, 2004, Singapore.
V. T. Sam, P. Y. Ho, and Jack Y. B. Lee, "A First Look at the Properties of Many-to-One Data Flows,"
to appear in the Workshop on Parallel and Distributed Multimedia held in conjunction with the International Conference on Parallel Processing (ICPP 2006), Ohio, USA, August 14, 2006.
P. Y. Ho and Jack Y. B. Lee, "QoS-aware Adaptive Buffering for Multi-Source Video Streaming over
the Internet", to appear in IEEE GLOBECOM 2006.
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