Quality enhancement for scalable video delivery over wireless networks based on particle swarm optimization
In this paper, the main objective is to find an optimal rate allocation strategy that can maximize the total-weighted quality of experience (QoE) associated with multiple video sources over error-prone multi-hop wireless networks based on the particle swarm optimization (PSO) technique which belongs to the family of swarm intelligence algorithms. 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 PSO in which by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized. Second, these optimal rates have been used for differentiated QoE 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 sequences quality and can be associated with some previous service level agreement-based prices. A strong motivation for differentiated quality enforcement is that video sources need to be encoded differently for different resolutions to cater to diverse devices from mobile displays to HDTV displays. Some numerical analysis have been presented to validate the theoretical results and to verify the claims.
qoe pso mos gap si pep