This paper considers the Softcast joint source-channel video coding scheme for data transmission over parallel channels with different power constraints and noise characteristics, typical in DSL or PLT channels. To minimize the mean square error at receiver, an optimal precoding matrix design problem has to be solved, which requires the solution of an inverse eigenvalue problem. Such solution is taken from the MIMO channel precoder design literature. Alternative suboptimal precoding matrices are also proposed and analyzed, showing the efficiency of the optimal precoding matrix within Softcast, which provides gains increasing with the encoded video quality.

Blind Source Separation (BSS) deals with the recovery of source signals from a set of observed mixtures, when little or no knowledge of the mixing process is available. BSS can find an application in the context of network coding, where relaying linear combinations of packets maximizes the throughput and increases the loss immunity. By relieving the nodes from the need to send the combination coefficients, the overhead cost is largely reduced. However, the scaling ambiguity of the technique and the quasi-uniformity of compressed media sources makes it unfit, at its present state, for multimedia transmission. In order to open new practical applications for BSS in the context of multimedia transmission, we have recently proposed to use a non-linear encoding to increase the discriminating power of the classical entropy-based separation methods. Here, we propose to append to each source a non-linear message digest, which offers an overhead smaller than a per-symbol encoding and that can be more easily tuned. Our results prove that our algorithm is able to provide high decoding rates for different media types such as image, audio, and video, when the transmitted messages are less than 1.5 kilobytes, which is typically the case in a realistic transmission scenario.

In Blind Source Separation, or BSS, a set of source signals are recovered from a set of mixed observations without knowledge of the mixing parameters. Originated for real signals, BSS has recently been applied to finite fields, enabling more practical applications. However, classical entropy-based techniques do not perform well in finite fields. Here, we propose a non-linear encoding of the sources to increase the discriminating power of the separation methods. Our results show that the encoding improves the success rate of the separation for sources with few samples in large finite fields, both conditions met in practical networking applications. Our results open new possibilities in the context of network coding-wherein linear combinations of packets are sent in order to maximize throughput and increase loss immunity- by relieving the nodes from the need to send the combination coefficients, thus reducing the overhead cost.

Network coding (NC) can achieve the maximum information flow in the network by allowing nodes to combine received packets before retransmission. Several papers have shown NC to be beneficial in mobile ad-hoc networks, but the delay introduced by buffered decoding raises a problem in real-time streaming applications. Here we propose to use NC jointly with multiple description coding (MDC) to allow instant decoding of the received packets. The optimal encoding coefficients are chosen via distributed optimisation of the expected video quality. Nodes receive up-to-date information about the network topology through a recently proposed protocol, originally designed for real-time streaming of MDC video. Results show that, due to the limitations imposed by instant decoding to the coding window size, our approach consistently outperforms the popular technique of random linear network coding.

Multi-view video streaming is an emerging video paradigm that enables new interactive services, such as free viewpoint television and immersive teleconferencing. However, it comes with a high bandwidth cost, as the equivalent of many single-view streams has to be transmitted. Network coding (NC) can improve the performance of the network by allowing nodes to combine received packets before retransmission. Several works have shown NC to be beneficiai in wireless networks, but the delay introduced by buffering before decoding raises a problem in real-time streaming applications. Here, we propose to use Expanding Window NC (EWNC) for multi-view streaming to allow immediate decoding of the received packets. The order in which the packets are included in the coding window is chosen via RD-optimization for the current sending opportunity. Results show that our approach consistently outperforms both classical NC applied on each view independently and transmission without NC.