Distributed soft video transmission based on hybrid digital and analog scheme

Abstract

This study presents a novel distributed soft video delivery scheme using hybrid digital and analog framework, which realizes a relatively lightweight encoder as well as good robustness. Specifically, after applying scalar quantization to the interframes, the scaled analog information (quantization error) and the scaled digital information (quantized source) are superimposed and then transmitted. In this way, the proposed scheme directly delivers pseudo-analog symbols over the orthogonal frequency division multiplexing (OFDM) channels and involves no complicated digital codec. Moreover, we utilize cross-frame correlation to implement a distributed paradigm which further reduces the encoder complexity since the complex motion estimation and compensation algorithms are transferred to the decoder. Accordingly, the power distortion optimization scheme, which resolve the allocation of power and the parameters optimization to achieve minimum transmission distortion, is proposed. To solve it, first, we formulate the power distortion expressions regarding quantization parameters and power allocation coefficients. Subsequently, we divide the problem into two sub-problems based on the fast coordinate descent method and further propose a greedy iterative algorithm to optimize them. We also develop a data-driven optimization algorithm based on deep learning that reduces the additional delay brought by the iterative optimization method. At the receiver, we estimate the quantization output and quantization error by the modified linear least squares estimation with the virtual noise variance. Based on the simulation results, the proposed framework has a better performance in terms of peak signal-to-noise ratio and structural similarity than the relevant cutting-edge schemes while maintaining good robustness.