Arvind Prasanna’s research while affiliated with University at Buffalo, State University of New York and other places

This article presents the design of a networked system for joint compression, rate control and error correction of video over wireless multimedia sensor networks (WMSNs) based on the theory of compressed sensing. First, compressed sensing based video encoding for transmission over WMSNs is studied. It is shown that compressed sensing can be used to 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 different videos 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. Finally, the entire system is evaluated through simulation and software-defined testbed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality.

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to co-design a low-complexity video encoder based on compressed sensing and a rate-adaptive streaming protocol for wireless video transmission. The proposed rate control scheme is designed with the objectives to maximize the received video quality at the receiver and to prevent network congestion while maintaining fairness between multiple video transmissions. Video distortion is represented through analytical and empirical models and minimized based on a new cross-layer control algorithm that jointly regulates the video encoding rate and the channel coding rate at the physical layer based on the estimated channel quality. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. The proposed scheme is shown to outperform TCP-Friendly Rate Control (TFRC).

Data loss in wireless communications greatly affects the reconstruction quality of wirelessly transmitted images. Conventionally, channel coding is performed at the encoder to enhance recovery of the image 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 (AP) 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.