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Advances in Multimedia
Volume 2007 (2007), Article ID 76846, 11 pages
Research Article

Distortion Optimized Packet Scheduling and Prioritization of Multiple Video Streams over 802.11e Networks

1Department of Electrical & Computer Engineering, University of Patras, Greece
2Mobility Applications Laboratory - Networks, Ericsson Deutschland GmbH - Eurolab, Germany
3Department of Information & Communication Systems Engineering, University of Aegean, Greece
4Department of Communication Systems and Networks, Technological Educational Institute of Messolonghi, Greece

Received 11 June 2007; Accepted 20 August 2007

Academic Editor: Stavros Kotsopoulos

Copyright © 2007 Ilias Politis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a generic framework solution for minimizing video distortion of all multiple video streams transmitted over 802.11e wireless networks, including intelligent packet scheduling and channel access differentiation mechanisms. A distortion prediction model designed to capture the multireferenced frame coding characteristic of H.264/AVC encoded videos is used to predetermine the distortion importance of each video packet in all streams. Two intelligent scheduling algorithms are proposed: the “even-loss distribution,” where each video sender is experiencing the same loss and the “greedy-loss distribution” packet scheduling, where selected packets are dropped over all streams, ensuring that the most significant video stream in terms of picture context and quality characteristics will experience minimum losses. The proposed model has been verified with actual distortion measurements and has been found more accurate than the “additive distortion” model that omits the correlation among lost frames. The paper includes analytical and simulation results from the comparison of both schemes and from their comparison to the simplified additive model, for different video sequences and channel conditions.