Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
An Efficient Hardware Implementation of Feed-Forward Neural Networks
Applied Intelligence
Supporting excess real-time traffic with active drop queue
IEEE/ACM Transactions on Networking (TON)
Congestion Control Based on Priority Drop for H.264/SVC
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Not All Packets Are Equal, Part 2: The Impact of Network Packet Loss on Video Quality
IEEE Internet Computing
A unified traffic model for MPEG-4 and H.264 video traces
IEEE Transactions on Multimedia
Evaluation of single rate multicast congestion control schemes for MPEG-4 video transmission
NGI'09 Proceedings of the 5th Euro-NGI conference on Next Generation Internet networks
Incorporating packet semantics in scheduling of real-time multimedia streaming
Multimedia Tools and Applications
Selective packet discard in mobile video delivery based on macroblock-based distortion estimation
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
Image Quality Metrics: PSNR vs. SSIM
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
On the impact of adaptive RED in IP networks transporting H.264/MPEG-4 AVC video streams
Computers and Electrical Engineering
An overview of quality of experience measurement challenges for video applications in IP networks
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
A Survey on Internet Traffic Identification
IEEE Communications Surveys & Tutorials
The H.264/MPEG4 advanced video coding standard and its applications
IEEE Communications Magazine
Traffic characteristics of H.264/AVC variable bit rate video
IEEE Communications Magazine
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The transmission of real-time multimedia streams requires service guarantees, such as limited packet loss, minimum bandwidth and low delay and jitter, to ensure a good quality of experience (QoE) for viewers. The spatial and temporal redundancy of videos is addressed by coding algorithms that reduce the amount of information necessary to represent the images. As a consequence, multimedia traffic commonly presents variable bit rate behavior and self-similar characteristics. Although the reduction in bandwidth requirements is highly desirable, the burstiness of traffic leads to problems in network design and performance prediction. Even a low level of packet loss could severely affect the viewer QoE. In this paper, we propose a real-time packet payload classifier, implemented with artificial neural network (ANN) to be used at network routers. A priority packet discard strategy can be implemented to avoid discarding packets that carry the most relevant information for image reconstruction, thus improving the perceived quality. This approach does not require changes at the video source to classify outgoing packets. The ANN was employed because of its good capacity in temporal series recognition and the possibility of its implementation in real-time systems due to its low computational complexity. The video traces used for training and validation were encoded with H.264/MPEG-4 Advanced Video Coding and are publicly available. The priority packet discard strategy was tested through computational simulations. The QoE was estimated comparing the peak signal-to-noise ratio (PSNR) of original and the received frames of video, and the results indicate that the proposed method improves the QoE. The implementation does not require packet payload processing and can be performed with network layer information only.