Nan Cen

Video Transmission Over Lossy Wireless Networks: A Cross-Layer Perspective

Video content currently makes up nearly half of the “fixed” Internet traffic and more than a third of the mobile traffic in North America, with most other regions showing similar trends. As mobile data rates continue to increase and more people rely on 802.11 wireless for home and commercial Internet access, the amount of video transmitted over at least one wireless hop will likely continue to increase. In addition, as cameras continue to become smaller and cheaper, the demand for video services in sensor and MANET networks will also increase. In this paper, we examine the state of the art of wireless video communication at each layer of the networking stack. We consider both existing and emerging technologies at each layer of the protocol stack as well as cross-layer designs, and discuss how these solutions can increase the video experience for the end user.

Implementation of a 2x2 MIMO-OFDM Real-Time System on DSP/FPGA Platform

In this paper we present a 2x2 MIMO-OFDM real-time system based on Sundances Software Radio development kits, which includes TI DSP, Xilinx FPGA, ADC/DAC and RF modules, etc. As a demonstration, we implement an MIMO-OFDM system, which is a simplification version of two antennas IEEE802.11n standard, on this platform to verify its availability. At transmitter it contains OFDM packet generation, encoding of Alamouti STBC and DUC, and DDC. At receiver, the system contains some necessary receiver algorithms, such as OFDM synchronization, channel estimation, decoding of Alamouti STBC, soft demapper and other components. In addition, the designed platform has greater feasibility and can be used for performance evaluation of newly developed signal processing algorithms.

Multi-view Wireless Video Streaming Based on Compressed Sensing: Architecture and Network Optimization

Multi-view wireless video streaming has the potential to enable a new generation of efficient and low-power pervasive surveillance systems that can capture scenes of interest from multiple perspectives, at higher resolution, and with lower energy consumption. However, state-of-the-art multi-view coding architectures require relatively complex predictive encoders, thus resulting in high processing complexity and power requirements. To address these challenges, we consider a wireless video surveillance scenario and 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. It also uses estimates of image sparsity to perform efficient rate adaptation and effectively exploit inter-view correlation at the encoder side. Based on the proposed encoding/decoding architecture, we further develop a CS-based end-to-end rate distortion model by considering the effect of packet losses on the perceived video quality. We then introduce a modeling framework to design network optimization problems in a multi-hop wireless sensor network. Extensive performance evaluation results show that the proposed coding framework and power-minimizing delivery scheme are able to transmit multi-view streams with guaranteed video quality at low power consumption.

Joint decoding of independently encoded compressive multi-view video streams

We design a video coding and decoding framework for multi-view video systems based on compressed sensing imaging principles. Specifically, we focus on joint decoding of independently encoded compressively-sampled multi-view video streams. We first propose a novel distributed coding/decoding architecture designed to leverage inter-view correlation through joint decoding of the received compressively-sampled frames. At the encoder side, we select one view (referred to as K-view) as a reference for the other views (referred to as CS-views). The video frames of the CS-view are encoded and transmitted at a lower measurement rate than those of the selected K-view. At the decoder side, we generate side information to decode the CS-views as follows. First, each K-view frame is down-sampled and reconstructed, and then compared with the initially reconstructed CS-view frame to obtain an estimate of the inter-view motion vector. The original CS-view measurements are then fused with the generated side image to reconstruct the CS-view frame through a newly designed algorithm that operates in the measurement domain. We also propose a blind video quality estimation method that can be used within the proposed framework to design channel-adaptive rate control algorithms for quality-assured multi-view video streaming. We extensively evaluate the proposed scheme using real multi-view video traces. Results indicate that up to 1.6 dB improvement in terms of PSNR can be achieved by the proposed scheme compared with traditional independent decoding of CS frames.

Interview Motion Compensated Joint Decoding for Compressively Sampled Multiview Video Streams

In this paper, we design a novel multiview video encoding/decoding architecture for wirelessly multiview video streaming applications, e.g., 360 degrees video, Internet of Things (IoT) multimedia sensing, among others, based on distributed video coding and compressed sensing principles. Specifically, we focus on joint decoding of independently encoded compressively sampled multiview video streams. We first propose a novel side-information (SI) generation method based on a new interview motion compensation algorithm for multiview video joint reconstruction at the decoder end. Then, we propose a technique to fuse the received measurements with resampled measurements from the generated SI to perform the final recovery. Based on the proposed joint reconstruction method, we also derive a blind video quality estimation technique that can be used to adapt online the video encoding rate at the sensors to guarantee desired quality levels in multiview video streaming. Extensive simulation results of real multiview video traces show the effectiveness of the proposed fusion reconstruction method with the assistance of SI generated by an interview motion compensation method. Moreover, they also illustrate that the blind quality estimation algorithm can accurately estimate the reconstruction quality.

LANET: Visible-light ad hoc networks

Abstract Visible light communication (VLC) is a wireless technology complementary to well-understood radio frequency (RF) communication that is promising to help alleviate the spectrum crunch problem in overcrowded RF spectrum bands. While there has been significant advancement in recent years in understanding physical layer techniques for visible light point-to-point links, the core problem of developing efficient networking technology specialized for visible-light networks is substantially unaddressed. This article discusses the current existing techniques as well as the main challenges for the design of visible-light ad hoc networks - referred to as LANETs. The paper discusses typical architectures and application scenarios for LANETs and highlights the major differences between LANETs and traditional mobile ad hoc networks (MANETs). Enabling technologies and design principles of LANETs are analyzed and existing work is surveyed following a layered approach. Open research issues in LANET design are also discussed, including long-range visible light communication, full-duplex LANET MAC, blockage-resistant routing, VLC-friendly TCP and software-defined prototyping, among others.