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Hybrid digital–analog (HDA) transmission is beco-ming an attractive solution for mobile video delivery because it not only has graceful degradation with channel variations but also yields high power efficiency. However, the heavy bandwidth demand of analog transmission is still an unsolved problem, limiting the received video quality when the bandwidth is not sufficient. To address this problem, we propose adopting Shannon–Kotel’nikov (SK) mapping for HDA video transmission and design an HDA scheme called SK-Cast. SK-Cast consists of a digital and an analog branch. In the digital branch, SK-Cast compresses the video sequence using an high efficiency video coding digital encoder to produce a base layer. The base layer is transmitted through digital methods with strong protection. The residual signals are then decorrelated using three-dimensional discrete cosine transform transform. The SK mapping is exploited to transmit these coefficients, as they can achieve efficient bandwidth compression. We address the resource allocation problems in SK-Cast, including the allocation between digital and analog branches and the allocation among analog symbols. The simulation results show that the SK-Cast outperforms the state-of-the-art HDA systems, including WSVC and SharpCast, and a digital scalable video coding system. [ABSTRACT FROM AUTHOR]

An estimation setting is considered, where a number of sensors transmit their observations of a physical phenomenon, described by one or more random variables, to a sink over noisy communication channels. The goal is to minimize a quadratic distortion measure (Minimum Mean Square Error - MMSE) under a global power constraint on the sensors' transmissions. Linear MMSE encoders and decoders, parametrically optimized in encoders' gains, Shannon-Kotel'nikov mappings, and nonlinear parametric functional approximators (neural networks) are investigated and numerically compared, highlighting subtle differences in sensitivity and achievable performance. [ABSTRACT FROM AUTHOR]

This paper deals with lossy joint source-channel coding for transmitting memoryless sources over AWGN channels. The scheme is based on the geometrical interpretation of communication by Kotel'nikov and Shannon where amplitude-continuous, time-discrete source samples are mapped directly onto the channel using curves or planes. The source and channel spaces can have different dimensions and thereby achieving either compression or error control, depending on whether the source bandwidth is smaller or larger than the channel bandwidth. We present a general theory for 1:N and M:1 dimension changing mappings, and provide two examples for a Gaussian source and channel where we optimize both a 2:1 bandwidth-reducing and a 1:2 bandwidth-expanding mapping. Both examples show high spectral efficiency and provide both graceful degradation and improvement for imperfect channel state information at the transmitter. [ABSTRACT FROM AUTHOR]