Compressed Sensing based Low-Power Multi-view Video Coding and Transmission in Wireless Multi-path Multi-hop Networks

Abstract : Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption.
 EXISTING SYSTEM :
 ? We consider both existing and emerging technologies at each layer of the protocol stack as well as cross-layer designs, and discuss how both these solutions can increase the video experience for the end user. ? Several projects have looked at designing videoenabled daughter cards which plug into existing wireless motes. ? In most cases, the sensor networks either take advantage of existing devices networks (such as using existing smartphones to monitor an urban environment), or deploy specific single-use devices. ? However, they then propose a new end-to-end solution that would limit the rate of video packets into the network based on the video playback rate, but without the burstiness of the existing implementation.
 DISADVANTAGE :
 ? The signal estimation step consists of solving a least squares problem which is both time and resources consuming as it includes both matrix inversion and matrix-vector multiplication. ? Furthermore, the same authors further optimized their implementation by adopting Q-R decomposition (QRD) to solve the LS problem in . ? In, the authors presented a comparative study between the implementation of the OMP for image reconstruction using a high parallel computation with an LU decomposition for solving the LS problem. ? Although CS was developed as a sensing/compression paradigm, it can be extended to other domains such as signal detection and sparse problem optimization.
 PROPOSED SYSTEM :
 • In, unlike the previous TCP applications we have discussed, the proposed system adapts the video quality to the channel quality. • By explicitly introducing packet arrival deadline constraints in the optimization problem in, the proposed routing algorithm guarantees good perceived video quality while attempting to satisfy stringent delay requirements for real-time video services. • To address these challenges, a plethora of video-application-aware MAC protocols have been proposed. • Therefore, the proposed scheme guarantees that nodes with real-time video packets have priority access over those with delay tolerant packets. • Moreover, a mini-slot reuse policy is proposed by allowing two devices with more than two-hop distance to use the same mini-slot.
 ADVANTAGE :
 ? In general, remote sensing devices are battery-driven, hence their performance is prone to the limited battery lifetime leading to both poor integration and user adherence. ? However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. ? Optimal or near-optimal reconstruction performance: The measured data maintain the salient information of the signal for reconstruction purposes. ? The performance of these recovery algorithms depends on the targeted application and no unique recovery metric is established to determine the best recovery technique for all scenarios.

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