Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures <s> DVC in wireless sensor networks <s> This paper addresses the important aspect of compressing and transmitting video signals generated by wireless broadband networks while heeding the architectural demands imposed by these networks in terms of energy constraints as well as the channel uncertainty related to the wireless communication medium. Driven by the need to develop light, robust, energy-efficient, and low delay video delivery schemes, a distributed video coding based framework dubbed PRISM is introduced. PRISM addresses the wireless video sensor network requirements far more effectively than current state-of-the-art standards like MPEG. This paper focuses on the case of a single video camera and use it as a platform to describe the theoretical principles and practical aspects underlying distributed video coding. <s> BIB001 </s> Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures <s> DVC in wireless sensor networks <s> The availability of low-cost hardware such as CMOS cameras and microphones has fostered the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., networks of wirelessly interconnected devices that are able to ubiquitously retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, the state of the art in algorithms, protocols, and hardware for wireless multimedia sensor networks is surveyed, and open research issues are discussed in detail. Architectures for WMSNs are explored, along with their advantages and drawbacks. Currently off-the-shelf hardware as well as available research prototypes for WMSNs are listed and classified. Existing solutions and open research issues at the application, transport, network, link, and physical layers of the communication protocol stack are investigated, along with possible cross-layer synergies and optimizations. <s> BIB002 </s> Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures <s> DVC in wireless sensor networks <s> A wireless sensor network with multimedia capabilities typically consists of data sensor nodes, which sense, for instance, sound or motion, and video sensor nodes, which capture video of events of interest. In this survey, we focus on the video encoding at the video sensors and the real-time transport of the encoded video to a base station. Real-time video streams have stringent requirements for end-to-end delay and loss during network transport. In this survey, we categorize the requirements of multimedia traffic at each layer of the network protocol stack and further classify the mechanisms that have been proposed for multimedia streaming in wireless sensor networks at each layer of the stack. Specifically, we consider the mechanisms operating at the application, transport, network, and MAC layers. We also review existing cross-layer approaches and propose a few possible cross-layer solutions to optimize the performance of a given wireless sensor network for multimedia streaming applications. <s> BIB003 </s> Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures <s> DVC in wireless sensor networks <s> Recently the development of Distributed Video Coding (DVC) has provided the promising theory support to realize the infrastructure of Wireless Multimedia Sensor Network (WMSN), which composed of autonomous hardware for capturing and transmission of quality audio-visual content. The implementation of DVC in WMSN can better solve the problem of energy constraint of the sensor nodes due to the benefit of lower computational encoder in DVC. In this paper, a practical DVC scheme, pixel-domain Wyner-Ziv(PDWZ) video coding, with slice structure and adaptive rate selection(ARS) is proposed to solve the certain problems when applying DVC into WMSN. Firstly, the proposed slice structure in PDWZ has extended the feasibility of PDWZ to work with any interleaver size used in Slepian-wolf turbo codec for heterogeneous applications. Meanwhile, based on the slice structure, an adaptive code rate selection has been proposed aiming at reduce the system delay occurred in feedback request. The simulation results clearly showed the enhancement in R-D performance and perceptual quality. It also can be observed that system delay caused by frequent feedback is greatly reduced, which gives a promising support for WMSN with low latency and facilitates the QoS management. <s> BIB004 </s> Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures <s> DVC in wireless sensor networks <s> Wireless video sensor networks (WVSNs) have drawn significant attention in recent years due to the advent of low-cost miniaturized cameras, which makes it feasible to realize large-scale WVSNs for a variety of applications including security surveillance, environmental tracking, and health monitoring. However, the conventional video coding paradigms are not suitable for WVSNs due to resource constraints such as limited computation power, battery energy, and network bandwidth. In this paper, we evaluated and analyzed the performance of video codecs based on emerging video coding paradigms such as distributed video coding and distributed compressive video sensing for multihop WVSNs. The main objective of this work was to provide an insight into the computational (encoding/decoding) complexity, energy consumption, node and network lifetime, processing and memory requirements, and the quality of reconstruction of these video codecs. Based on the findings, this paper also provides some guidelines for the selection of appropriate video codecs for a given WVSN application. <s> BIB005
Wireless sensor networks (WSNs) have gained considerable attention in recent years due to their vast application domain, flexible deployment structure, and most of all, the availability of low-cost CMOS sensor modules. The application domain for WSN spans from surveillance monitoring to healthcare, traffic and industrial control sectors  . The self-organizing, flexible, and easily scalable infrastructure of WSN is one key factor for its widespread popularity. On the other hand, WSNs have to deal with optimization of performance parameters such as delay, throughput, network lifetime, and quality of service (QoS)  . Moreover, WSN-based multimedia applications have limited bandwidth which necessitates the need for more efficient compression algorithms. Video streams are highly bandwidth demanding. Transmitting these streams to the sink via several intermediate nodes over radio links requires much higher bandwidth than that required for transmitting scalar data BIB005 . The high bit rate and multi-hop transmission make the system prone to congestion due to both intra-and inter-flow interferences. The congestion becomes more serious when there are multiple flows and where traffic exhibits a many-to-one pattern  . Different WSN applications may have different requirements in terms of bandwidth, processing and compression, among others. Video streaming involves continuous capturing and delivery of data that requires optimized encoding and compression algorithms in addition to efficient hardware to meet the often demanding QoS requirements BIB002 ). In the context of WSNs, the approaches for network layer QoS can be based on reliability or timeliness of video delivery BIB003 . For example, some applications are delay-tolerant, but require reliable and error-free data transmission. It involves packet retransmissions and multipath routing through which a sensor node can inject multiple copies of same packet into different paths so that at least one copy is able to make it to the sink. In this section, we also discuss the applicability of DVC in WSNs and how well it fits under its constrained environment. Almost all of the video coding applications fall within the two classes of application models, namely downlink and uplink models. The downlink application model is associated with the broadcasting approach, where low-complexity decoder is desirable and the complexity of the encoder is not an issue. The encoder of the downlink application model is more like a base-station that does not have computational constraints. Applications such as video streaming, broadcasting, and telephony are belonging to the downlink application model. On the other hand, the uplink application model, also known as wireless video model, represents the reverse architecture, where low-complexity encoder is required and the complexity of the decoder is not a major concern. Consider an environment which comprises of several integrated units (wireless video sensors) that include video sensing modules with on-board processing and transmission functionality. These integrated units are interconnected with each other via wireless networking protocol, communicate over radio links, and have limited battery life. They are used to capture, compress and transmit compressed video data from their surroundings to a centralize location (basestation) over single-or multi-hop environment. Applications such as mobile video calling, wireless video surveillance, and monitoring are belonging to the uplink application model   BIB001 BIB004 ). Popular video coding standards such as MPEGx and H.264/AVC supports only the downlink application model, while the DVC is a solution for applications of the uplink model. Using DVC in uplink application domain has potential advantages such as flexible allocation of functionality from encoder to decoder (and even transcoder in some cases), low-encoding complexity that in turn leads to low-power consumption, longer battery life, and light-weight processing devices . DVC theory introduces a notion of shifting computational complexity from encoder to decoder, which makes it a viable option for applications in WSN domain where encoders are resource-limited wireless sensor nodes, and decoder is a more powerful base-station. The rate-distortion performance of DVC codecs is also comparable to that of conventional H.264/AVC Intra-frame coding  . Another important feature of DVC which is desirable in WSNs is that it provides better error resilience. DVC does not employ any prediction loops at encoder and therefore no estimation and prediction errors are sent to the decoder. Rather, the side information module in DVC decoder predicts the frame by exploiting statistical and temporal correlation among video frames . Exploitation of correlation in multi-view camera architecture is also a distinguished feature of DVC for WSNs. For example, if multiple wireless video sensors have some overlapping coverage areas, they may not need to communicate with each other to exploit that correlation; rather they encode their video sequences independently and proceed with the transmission to the decoder, which is responsible for processing the correlation among video sequences from these sources . DVC exhibits higher computational complexity at decoder, which consequently makes it a less viable option for applications that requires real-time decoding in WSN. By itself, DVC is not sufficient to support an end-to-end wireless video surveillance system. For this purpose, the use of transcoder is mandatory to simplify both transmitter (encoding node) and receiver (decoding node) sides of the network. However, the middle tier, i.e. the transcoder, requires video to be encoded by conventional PVC based codec. The basic architecture of transcoder is shown in Fig. 4 .