Cross-layer optimization for 3-D video transmission over cooperative relay systems

• A novel optimization algorithm using Lagrange multiplier is proposed to solve the optimum problem.
• We investigate the impacts of relay selection on the video system performance.
• Estimation procedures of the total SNR are proposed to control the source and destination nodes.
• We investigate the impacts of total instantaneous SNR estimation on the video system performance.
• The performance of video system is very sensitive to the accuracy of the total SNR estimation.

The rate budget constraint and the available instantaneous signal-to-noise ratio of the best relay selection in cooperative systems can dramatically impact the system performance and complexity of video applications, since they determine the video distortion. By taking into account these constrained factors, we first outline the signal model and formulate the system optimization problem. Next, we propose a new approach to cross-layer optimization for 3-D video transmission over cooperative relay systems. We propose procedures for estimation of the end-to-end instantaneous signal-to-noise ratio using an estimate of the available instantaneous signal-to-noise ratios between the source–destination, and source–relay–destination before starting to send the video signal to the best relay and destination. A novel approach using Lagrange multipliers is developed to solve the optimum bit allocation problem. Based on the rate budget constraint and the estimated the end-to-end instantaneous signal-to-noise ratio, the proposed joint source–channel coding (JSCC) algorithm simultaneously assigns source code rates for the application layer, the number of high and low priority packets for the network layer, and channel code rates for the physical layer based on criteria that maximize the quality of video, whilst minimizing the complexity of the system. Finally, we investigate the impact of the estimated the end-to-end instantaneous signal-to-noise ratio on the video system performance and complexity. Experimental results show that the proposed JSCC algorithm outperforms existing algorithms in terms of peak signal-to-noise ratio. Moreover, the proposed JSCC algorithm is found to be computationally more efficient since it can minimize the overall video distortion in a few iterations.