Compressed Sensing based Low-Power Multi-View Video Coding and Transmitting 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.

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Publication Status

Early Access

Comments

Published online: 08 Jan 2021

Keywords and Phrases

Compressed Sensing; Decoding; Encoding; Image coding; Internet of Things; Multi-view Video Streaming; Network Optimization; Spread spectrum communication; Streaming media; Wireless communication; Wireless sensor networks

International Standard Serial Number (ISSN)

1536-1233; 1558-0660

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

08 Jan 2021

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