Scalable video transmission over multi-hop wireless networks with enhanced quality of experience using swarm intelligence

In this work, we take the advantages of the particle swarm optimization method which belongs to the family of swarm intelligence algorithms to find improved solutions for delivering digital video content with enhanced quality of experience to the end users over error-prone multi-hop wireless networks. In video transmission over such wireless networks, many network-based (packet loss, delay, etc.) and source-based (encoding quantization level, etc.) parameters can impair the perceived video quality. The main contributions of the proposed work are twofold. At first, an optimal bandwidth allocation framework is being developed based on the particle swarm optimization algorithm in which by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized. Secondly, these optimal rates have been used for differentiated quality of experience enforcement between multiple competing scalable video sources. The resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of a moderate scalable video encoder such as H.264/MPEG4 AVC. The aforementioned weight parameters are selected based on the importance of each video sequence's quality and can be associated with some previous service level agreement based prices. Some guidelines about the practical implementation of the proposed algorithm are given. Numerical analysis has been performed to validate the theoretical results and to verify the claims.