Joint Network-Source Video Coding Based on Lagrangian Rate Allocation

Joint network-source video coding (JNSC) is targeted to achieve the optimum delivery of a video source to a number of destinations over network with capacity constraints. In this paper, a practical scalable multiple description coding is proposed for JNSC, based on Lagrangian rate allocation and scalable video coding. After the spatiotemporal wavelet transformation of input video sequence and the bit plane coding and context-based adaptive binary arithmetic coding, jointing network-source coding is performed on the coding passes of the code blocks using Lagrangian rate allocation. The principle relationship of the rate-distortion slop ratio with receiving probability in network links is derived under the link capacity constraints. In this way, scalable multiple descriptions can be generated to optimize the delivery to be robust and adaptive to the dynamics of heterogeneous networks. The performance of the proposed scalable multiple description coding is explored in the Peer-to-Peer streaming network.