Scott Pudlewski

Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

This paper 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 (CS). 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 different videos while maximizing the received video quality. It is shown that the rate of Compressed Sensed Video (CSV) 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 test bed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. The test bed results show that the rates converge to stable values in real channels.

On the Performance of Compressive Video Streaming for Wireless Multimedia Sensor Networks

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to study performance limits and outline key design principles that will be the basis for cross-layer protocol stacks for efficient transport of compressive video streams. Hence, this paper investigates the effect of key video parameters (i.e., quantization, CS samples per frame, and channel encoding rate) on the received video quality of CS images transmitted through a wireless channels. It is shown that, unlike JPEG-encoded images, CS-encoded images exhibit an inherent resiliency to channel errors, caused by the unstructured image representation; this leads to basically zero loss in image quality for random channel bit error rates as high as 10−4, and low degradation up to 10−3. Furthermore, it is shown how, unlike traditional wireless imaging systems, forward error correction is not beneficial for wireless transmission of CS images. Instead, an adaptive parity scheme that drops samples in error is proposed and shown to improve image quality. Finally, we present our initial investigations on a low-complexity, adaptive video encoder that performs low-complexity motion estimation.

A distortion-minimizing rate controller for wireless multimedia sensor networks

The availability of inexpensive CMOS cameras and microphones that can ubiquitously capture multimedia content from the environment is fostering the development of wireless multimedia sensor networks (WMSNs), i.e., distributed systems of wirelessly networked devices that can retrieve video and audio streams, still images, and scalar sensor data. A new cross-layer rate control scheme for WMSNs is introduced in this paper with a twofold objective: (i) maximize the video quality of each individual video stream; (ii) maintain fairness in the domain of video quality between different video streams. The rate control scheme is based on analytical and empirical models of video distortion and consists of a new cross-layer control algorithm that jointly regulates the end-to-end data rate, the video quality, and the strength of the channel coding at the physical layer. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. Once the end-to-end data rate has been determined, the sender adjusts the video encoder rate and the channel encoder rate based on the overall rate and the current channel quality, with the objective of minimizing the distortion of the received video. Simulations show that the proposed algorithm considerably improves the received video quality with respect to state-of-the art rate control algorithms, without sacrificing on fairness.

Compressive Video Streaming: Design and Rate-Energy-Distortion Analysis

Real-time encoding and error-resilient wireless transmission of multimedia content using traditional encoding techniques requires relatively high processing and transmission power, while pervasive surveillance and monitoring systems often referred to as wireless multimedia sensor networks (WMSNs) are generally composed of low-power, low-complexity devices. To bridge this gap, this article introduces and analyzes a compressive video sensing (CVS) encoder designed to reduce the required energy and computational complexity at the source node. The proposed encoder leverages the properties of compressed sensing (CS) to overcome many of the limitations of traditional encoding techniques, specifically lack of resilience to channel errors, and high computational complexity. Recognizing the inadequacy of traditional rate-distortion analysis to account for the constraints introduced by resource-limited devices, we introduce the notion of rate-energy-distortion, based on which we develop an analytical/empirical model that predicts the received video quality when the overall energy available for both encoding and transmission of each frame of a video is fixed and limited and the transmissions are affected by channel errors. The model allows comparing the received video quality, computation time, and energy consumption per frame of different wireless streaming systems, and can be used to determine the optimal allocation of encoded video rate and channel encoding rate for a given available energy budget. Based on the proposed model, we show that the CVS video encoder outperforms (in an energy constrained system) two common encoders suitable for a wireless multimedia sensor network environment; H.264/AVC intra and motion JPEG (MJPEG). Extensive results show that CVS is able to deliver video at good quality (an SSIM value of 0.8) through lossy wireless networks with lower energy consumption per frame than competing encoders.

Distributed Spectrum Sharing for Video Streaming in Cognitive Radio Ad Hoc Networks

A distributed joint routing and spectrum sharing algorithm for video streaming applications over cognitive radio ad hoc networks is proposed in this article. The proposed cross-layer control scheme dynamically allocates routes, spectrum and power to maximize the network throughput under the constraints posed by delay-sensitive video applications. The algorithm evaluates the expected delay of competing flows in single-hop and two-hop networks considering the time-varying spectrum condition and occupancy, traffic characteristics, and the condition of queues at intermediate nodes. Simulation results show that the proposed algorithm significantly reduces the packet loss rate and improves the average peak signal-to-noise ratio (PSNR) of the received video streams.

C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to develop a rate adaptive video streaming protocol for compressive sensed video, integrated with a new video encoder based on compressed sensing. The proposed rate control scheme is developed 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 minimized through analytical and empirical models and based on a new cross-layer control algorithm that jointly regulates the video quality and the strength of the channel coding at the physical layer. 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 traditional rate control schemes.

A Rate-Energy-Distortion Analysis for Compressed-Sensing-Enabled Wireless Video Streaming on Multimedia Sensors

Real-time encoding and error-resilient wireless transmission of multimedia content require high processing and transmission power. This paper investigates the rate-distortion performance of video transmission over lossy wireless links for low-complexity multimedia sensing devices with a limited budget of available energy per video frame. An analytical/empirical model is developed to determine the received video quality when the overall energy allowed for both encoding and transmitting each frame of a video is fixed and the received data is affected by channel errors. The model is used to compare the received video quality, computation time, and energy consumption per frame of different wireless streaming systems. Furthermore, it is used to determine the optimal allocation of encoded video rate and channel encoding rate for a given available energy budget. The proposed model is then applied to compare the energy-constrained wireless streaming performance of three encoders suitable for a wireless multimedia sensor network environment; H.264, motion JPEG (MJPEG) and our recently developed compressed sensing video encoder (CSV). Extensive results show that CSV, thanks to its low complexity, and to a video representation that is inherently resilient to channel errors, is able to deliver video at good quality (an SSIM value of 0.8) through lossy wireless networks with lower energy consumption per frame than competing encoders.

DMRC: Distortion-minimizing rate control for Wireless Multimedia Sensor Networks

The availability of inexpensive CMOS cameras and microphones that can ubiquitously capture multimedia content from the environment is fostering the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., distributed systems of wirelessly networked devices that can retrieve video and audio streams, still images, and scalar sensor data. WMSNs require the sensor network paradigm to be re-thought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS). A new rate control scheme for WMSNs is introduced in this paper with a two-fold objective: i) maximize the video quality of each individual video stream; ii) maintain fairness in video quality between different video streams. The rate control scheme is based on both analytical and empirical models and consists of a new cross-layer control algorithm that jointly regulates the end-to-end data rate, the video quality, and the strength of the channel coding at the physical layer. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. Once the end-to-end data rate has been determined, the sender adjusts the video encoder rate and the channel encoder rate based on the overall rate and the current channel quality, with the objective of minimizing the distortion of the received video. Simulations show that the proposed algorithm considerably improves the received video quality without sacrificing fairness.

Resilient image sensor networks in lossy channels using compressed sensing

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.

A hybrid Multi Meshed Tree routing protocol for wireless ad hoc networks

A proactive routing protocol called multi-mesh tree (MMT) was developed for use in wireless ad hoc network to extend connectivity from an Internet gateway to around 20 mobile nodes in a city area. In the work presented here, we extend MMT to wireless ad hoc networks of around one hundred nodes through a clustering algorithm that is integrated into the MMT creation. The proposed scheme uses a hybrid approach, where the proactive MMT is used for intra cluster routing while a reactive MMT (RMMT) introduced in this article is used for inter cluster routing. We further propose a novel route discovery and route recording scheme using route request and route response messages but has low flooding overheads and exhibits high route stability under high node mobility conditions. We apply the proposed RMMT scheme to provide connectivity among moving teams of ground troops and present simulation results based on a study of this scenario.