The application of neural networks to improve the quality of experience of video transmission over IP networks

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