Abstract-This article1 presents the design of a networked system for joint compression, rate control and error correction of video over resource-constrained embedded devices based on the theory of compressed sensing. The objective of this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the channel coding rate to maximize the received video quality. First, compressed sensing based video encoding for transmission over wireless multimedia sensor networks (WMSNs) is studied. It is shown that compressed sensing can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among video streams while maximizing the received video quality. It is shown that the rate of compressed sensed video can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy channels. Finally, the entire system is evaluated through simulation and testbed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. Testbed results also show that the rates converge to stable values in real channels.

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Abstract-Data loss in wireless communications greatly affects the reconstruction quality of a signal. In the case of images, data loss results in a reduction in quality of the received image. Conventionally, channel coding is performed at the encoder to enhance recovery of the signal by adding known redundancy. While channel coding is effective, it can be very computationally expensive. For this reason, a new mechanism of handling data losses in Wireless Multimedia Sensor Networks (WMSN) using Compressed Sensing (CS) is introduced in this paper. This system uses compressed sensing to detect and compensate for data loss within a wireless network. A combination of oversampling and an adaptive parity scheme are used to determine which CS samples contain bit errors, remove these samples and transmit additional samples to maintain a target image quality A study was done to test the combined use of adaptive parity and compressive oversampling to transmit and correctly recover image data in a lossy channel to maintain Quality of Information (QoI) of the resulting images. It is shown that by using the two components, an image can be correctly recovered even in a channel with very high loss rates of 10%. The AP portion of the system was also tested on a software defined radio testbed. It is shown that by transmitting images using a CS compression scheme with AP error detection, images can be successfully transmitted and received even in channels with very high bit error rates.