Chong Luo

Compressive data gathering for large-scale wireless sensor networks

This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.

Multimedia Cloud Computing

This article introduces the principal concepts of multimedia cloud computing and presents a novel framework. We address multimedia cloud computing from multimedia-aware cloud (media cloud) and cloud-aware multimedia (cloud media) perspectives. First, we present a multimedia-aware cloud, which addresses how a cloud can perform distributed multimedia processing and storage and provide quality of service (QoS) provisioning for multimedia services. To achieve a high QoS for multimedia services, we propose a media-edge cloud (MEC) architecture, in which storage, central processing unit (CPU), and graphics processing unit (GPU) clusters are presented at the edge to provide distributed parallel processing and QoS adaptation for various types of devices.

Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [I R] has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology. Second, although the sparsity of sensor readings is pervasive, it might be rather complicated to fully exploit it. Owing to the inherent flexibility of CS principle, the proposed CDG framework is able to utilize various sparsity patterns despite of a simple and unified data gathering process. In particular, we present approaches for adapting CS decoder to utilize cross-domain sparsity (e.g. temporal-frequency and spatial-frequency). We carry out simulation experiments over both synthesized and real sensor data. The results confirm that CDG can preserve sensor data fidelity at a reduced communication cost.

Continuous Network Coding in Wireless Relay Networks

Network coding has recently been applied to wireless networks and has achieved some initial success. Researches in wireless network coding have been mostly focusing on utilizing the broadcast nature of the wireless networks. In this paper, we propose a novel network coding framework for wireless relay networks that also takes into consideration the fading and error prone nature of the wireless networks. First, we extend the traditional network coding in lossless networks which operates on 0-1 bits, to a new framework which defines network coding on the posterior probability of each bit. This new framework allows an imperfect decode-recode process at a relay node and avoids possible error propagation when a hard decision is made at the relay node. It implicitly integrates decode-and-forward and estimate-and-forward strategies for wireless network coding to address the technical issues of channel fading and transmission errors. The proposed approach is validated through both theoretical analysis and extensive simulations. Both analysis and simulation confirm that this new framework is able to achieve significant gain over traditional network coding. This new framework also enables the introduction of adaptive scheme into network coding. We demonstrate a basic adaptation scheme and present some preliminary experimental results. The proposed adaptive scheme will lay down an essential foundation in this emerging field of wireless network coding in order to address issues related to link heterogeneity.

Resource allocation for cloud-based free viewpoint video rendering for mobile phones

Free viewpoint video (FVV) / Free viewpoint TV (FTV) on mobile devices over cellular networks is very challenging due to the requirement for large bandwidth and limitations in computation and battery life on mobile phones. To address such challenges, in this paper we propose a cloud-based FVV / FTV rendering framework for mobile devices over cellular networks. In this framework, cloud performs rendering for mobile devices. In order to achieve maximum QoE (Quality of Experience) for mobile users, we propose a novel resource allocation scheme, which jointly considers rendering allocation between cloud and client based on users QoE and rate allocation among texture, depth, and channel rate based on rate-distortion analysis. We formulate this resource allocation scheme as an optimization problem which can then be transformed into a convex optimization for the given rate ratio. Experimental results demonstrate that the proposed cloud-based FVV rendering solution can substantially improve video quality on mobile devices comparing with traditional approaches.

Seamless rate adaptation for wireless networking

This paper aims at designing a Seamless Rate Adaptation for wireless networking which achieves smooth rate adjustment in a broad dynamic range of channel conditions. Conventional rate adaptation can only achieve a stair-case rate adjustment. Even when combining with hybrid ARQ, it suffers from an irreconcilable conflict between throughput and dynamic range. We tackle this problem from a new perspective by relying on modulation, instead of channel coding, for rate adaptation. We propose rate compatible modulation (RCM), in which modulation signals are incrementally generated from information bits through weighted mapping. Rate adaptation is achieved through varying the number of modulated signals. As more signals are transmitted, information bits gradually accumulate energy. The weights in bit-to-symbol mapping are delicately designed to ensure fine-grained energy accumulation so that smoothness and efficiency can both be achieved. We design and implement a rate adaptation system, called SRA and evaluate its performance through a software radio testbed. Results show that, under highly dynamic channel conditions, SRA achieves over 80% throughput gain over 802.11a adaptive modulation and coding, and achieves 28.8% and 43.8% gain over HARQ systems implemented with Turbo code and Raptor code. We believe that the concept of rate compatible modulation opens up a fresh research avenue toward the wireless rate adaptation problem.

A Multiparty Videoconferencing System Over an Application-Level Multicast Protocol

Increased speeds of PCs and networks have made media communications possible on the Internet. Today, the need for desktop videoconferencing is experiencing robust growth in both business and consumer markets. However, the synchronous delivery of high-volume media content is still a big challenge under a current heterogeneous Internet environment. In this paper, we present a multiparty videoconferencing system based on a peer-to-peer (P2P) solution. The contribution of our paper is twofold. On the one hand, we design an application-level multicast scheme which intends to tolerate the heterogeneity in videoconferencing applications. Design tradeoffs are analyzed and our decisions are made based on extensive experimentation. On the other, we design a five-layer architecture for implementing a multiparty videoconferencing system. This architecture makes a clear-cut distinction between different functional modules and therefore provides rich flexibility in feature adaptation. We believe that our work can be a helpful reference in other efforts on building desktop videoconferencing systems.

Robust uncoded video transmission over wireless fast fading channel

This research studies robust uncoded video transmission over wireless fast fading channel, where only statistical channel state information (CSI) is available at the transmitter. We observe that increasing channel diversity for high priority (HP) data is essential to improving the robustness of video transmission in fading channels. By utilizing the noise and loss resilient nature of video, we find it possible to design a more robust system by re-allocating the power and channel uses among HP and LP (low priority) data. With total power and channel use constraints, we derive an optimal resource allocation scheme under the squared error distortion criterion. In particular, we first propose a new power allocation algorithm at given channel allocation. Second, based on the proposed power allocation algorithm, we design a channel allocation algorithm to strike the tradeoff between the diversity increase of HP data and the information loss of LP data. Third, under known noise power distribution, we derive the optimal resource allocation for uncoded video multicast. Simulations show that the proposed system achieves 2dB and 5dB gain in average and outage PSNR over Softcast in video unicast, and around 1.4dB and 4dB gain in multicast.

DigiMetro - an application-level multicast system for multi-party video conferencing

The increasing demand for multi-party videoconferencing has aroused the research interest in the underlying multicast support. In this paper, we propose DigiMetro, an application-level multicast system tailored to small and impromptu videoconferencing. Breaking through the conventional wisdom to use shared overlay to handle multiple data sources, DigiMetro organizes the data delivery routes as source-specific trees, which are first constructed by a local greedy algorithm and then gradually improved by a global refinement procedure. Extensive simulation experiments demonstrate the efficiency of both algorithms. Moreover, DigiMetro is able to handle different video bit rates and provide different services over voice/video streams.

Cactus: a hybrid digital-analog wireless video communication system

This paper challenges the conventional wisdom that video redundancy should be removed as much as possible for efficient communications. We discover that, by keeping spatial redundancy at the sender and properly utilizing it at the receiver, we can build a more robust and even more efficient wireless video communication system than existing ones. In the proposed framework, inter-frame (temporal) redundancy in video is removed at the encoder, but intra-frame (spatial) redundancy is retained. In doing so, pixel values after a transform-domain scaling are directly transmitted with amplitude modulation. At the receiver, spatial redundancy is utilized by image denoising. Note that denoising in our decoder is not a post-processing, but have to be immediately performed on channel output. We implement the video communication system called Cactus on SORA platform, and make the denoising processing real-time through GPU implementation. Cactus is evaluated in 802.11a/g WLAN environment. On average, Cactus outperforms SoftCast by 4.7 dB in video PSNR and is robust to packet losses.