An improved low-complexity multiple description coding for peer-to-peer video streaming

Multiple description scalable coding based on T+2D wavelet decomposition structure is highly flexible for peer-to-peer (P2P) video streaming. Finding the optimal truncation point of each code block (CB) within each description is an NP-hard problem. To implement an efficient low-complexity solution, we propose a simple clustering algorithm for partitioning the CBs into a limited number of clusters, such that one can find the optimal cluster-level redundancy-rate assignment matrix using a low-complexity full search. This approach improves the decoding quality compared to the co-echelon frameworks in which a non-optimal rate assignment matrix is used. In addition, the proposed clustering approach may be analytically represented by closed-form relations for low-complexity computation of optimal encoding parameters. The simulation results demonstrate that the adaptive proposed framework outperforms the approaches in by (0.25~1 dB), the scheme of by (1.3~1.6 dB), and the non-adaptive multiple description coding by (2.3~4.3 dB). Furthermore, the proposed clustering approach requires %52-%88 less computations compared to the framework in.