On the characterization of VBR MPEG streams
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Using adaptive linear prediction to support real-time VBR video under RCBR network service model
IEEE/ACM Transactions on Networking (TON)
Characterization of MPEG-4 Traffic over IEEE 802.11b Wireless LANs
LCN '02 Proceedings of the 27th Annual IEEE Conference on Local Computer Networks
A Fast Non-Linear Adaptive Algorithm for Video Traffic Prediction
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
IEEE Transactions on Mobile Computing
Video Traces for Network Performance Evaluation: A Comprehensive Overview and Guide on Video Traces and Their Utilization in Networking Research
IEEE Communications Surveys & Tutorials
ARROW: An Efficient Traffic Scheduling Algorithm for IEEE 802.11e HCCA
IEEE Transactions on Wireless Communications
IEEE Transactions on Multimedia
MPEG-4 and H.263 video traces for network performance evaluation
IEEE Network: The Magazine of Global Internetworking
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As the demand for broadband multimedia wireless services is increasing, improving quality of service (QoS) of the widely deployed IEEE 802.11 wireless LANs (WLANs) has become crucial. To support the QoS required by a wide range of applications, the IEEE 802.11 working group has defined a new standard—the IEEE 802.11e. Substantial studies have been performed on traffic scheduling for variable bit rate (VBR) video transport over 802.11e WLANs. However, within those studies, relatively little attention has been devoted to the QoS transmission of real-time live VBR videos. In this paper, we present a novel traffic scheduling algorithm for IEEE 802.11e that aims at achieving high channel utilization while still guaranteeing QoS requirements for real-time live VBR videos. The novel characteristic of this algorithm, compared to published literatures, is that it predicts the bandwidth requirements for future traffic using a novel traffic predictor designed to provide simple yet accurate online prediction. Analyses using real life MPEG video traces indicate that the proposed traffic predictor significantly outperforms previously published technique with respect to the prediction error. The proposed traffic predictor can also be used independently to estimate any MPEG traffic. The performance of the proposed traffic scheduling algorithm is also investigated by comparing several existing scheduling algorithms. Simulation results demonstrate that the proposed traffic scheduling algorithm surpasses other mechanisms in terms of channel utilization, buffer usage, video quality and packet loss rate.