Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers

In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC) in order to be resilient to channel loss while respecting the unequal levels of importance in the data, and data blocks are transmitted simultaneously in order to reduce decoding delays and improve the delivery performance. We formulate the optimal coding and scheduling decisions problem in our novel framework with the help of Markov decision processes (MDP), which are effective tools for modeling adapting streaming systems. Reinforcement learning approaches are then proposed to derive reduced computational complexity solutions to the adaptive coding and scheduling problems. The novel reinforcement learning approaches and the MDP solution are examined in an illustrative example for scalable video transmission . Our methods offer large performance gains over competing methods that deliver the data blocks sequentially. The experimental evaluation also shows that our novel algorithms offer continuous playback and guarantee small quality variations which is not the case for baseline solutions. Finally, our work highlights the advantages of reinforcement learning algorithms to forecast the temporal evolution of data demands and to decide the optimal coding and scheduling decisions

A multi-camera approach to image-based rendering and 3-D/Multiview display of ancient chinese artifacts

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Network coding meets multimedia: a review

While every network node only relays messages in a traditional communication system, the recent network coding (NC) paradigm proposes to implement simple in-network processing with packet combinations in the nodes. NC extends the concept of "encoding" a message beyond source coding (for compression) and channel coding (for protection against errors and losses). It has been shown to increase network throughput compared to traditional networks implementation, to reduce delay and to provide robustness to transmission errors and network dynamics. These features are so appealing for multimedia applications that they have spurred a large research effort towards the development of multimedia-specific NC techniques. This paper reviews the recent work in NC for multimedia applications and focuses on the techniques that fill the gap between NC theory and practical applications. It outlines the benefits of NC and presents the open challenges in this area. The paper initially focuses on multimedia-specific aspects of network coding, in particular delay, in-network error control, and mediaspecific error control. These aspects permit to handle varying network conditions as well as client heterogeneity, which are critical to the design and deployment of multimedia systems. After introducing these general concepts, the paper reviews in detail two applications that lend themselves naturally to NC via the cooperation and broadcast models, namely peer-to-peer multimedia streaming and wireless networkin

Image-based compression, prioritized transmission and progressive rendering of circular light fields (CLFS) for ancient Chinese artifacts

This paper proposes an efficient algorithm for the compression, prioritized transmission and progressive rendering of circular light field (CLF) for ancient Chinese artifacts. It employs wavelet coder to achieve spatial scalability and divide the compressed data into a lower resolution base layer and an additional enhancement layer. The enhancement layer is coded as in JPEG2000 into packets where the base-layer is coded using disparity compensation prediction (DCP). The frame structure is designed to provide efficient access to the compressed data in order to support selective transmission and decoding. The depth and alpha maps are coded analogously. A prioritized transmission scheme which support interactive progressive rendering is also proposed to further reduce the latency and response time of rendering. © 2010 IEEE.published_or_final_versionThe 2010 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Kuala Lumpur Malaysia, 6-9 December 2010. In IEEE APCCAS Proceedings, 2010, p. 340-34

Highly-Efficient Bulk Data Transfer for Structured Dissemination in Wireless Embedded Network Systems

Recent years have witnessed the remarkable development of wireless embedded network systems (WENS) such as cyber-physical systems and sensor networks. Reliable bulk data dissemination is an important building module in WENS, supporting various applications, e.g., remote software update, video distribution. The existing studies often construct network structures to enable time-slotted multi hop pipelining for data dissemination. However, the adopted transmission mechanism was originally designed for structureless protocols, and thus posing significant challenges on efficient structured data dissemination. In this paper, we investigate the problem of structured bulk data dissemination. Specifically, we propose reliable out-of-order transmission and bursty encoding mechanisms to transmit packets as many as possible in each transmission slot. As a consequence, the resulting transmission protocol (ULTRA) can fully utilize each transmission slot and propagate data in the network as fast as possible. The performance results obtained from both testbed and simulation experiments demonstrate that, compared to the state-of-the-art protocols, ULTRA can greatly enhance the dissemination performance by reducing the dissemination delay by 34.8%

Vehicle re-routing strategies for congestion avoidance

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This dissertation introduces a cost-effective and easily deployable vehicular re-routing system that reduces the effects of traffic congestion. The system collects real-time traffic data from vehicles and road-side sensors, and computes proactive, individually tailored re-routing guidance, which is pushed to vehicles when signs of congestion are observed on their routes. Subsequently, this dissertation proposes and evaluates two classes of re-routing strategies designed to be incorporated into this system, namely, Single Shortest Path strategies and Multiple Shortest Paths Strategies. These strategies are firstly implemented in a centralized system, where a server receives traffic updates from cars, computes alternative routes, and pushes them as guidance to drivers. The extensive experimental results show that the proposed strategies are capable of reducing the travel time comparable to a state-of-the-art Dynamic Traffic Assignment (DTA) algorithm, while avoiding the issues that make DTA impractical, such as lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows the system to be tuned to different levels of trade-off between re-routing effectiveness and computational efficiency. Also, the proposed traffic guidance system is robust even if many drivers ignore the guidance, or if the system adoption rate is relatively low. The centralized system suffers from two intrinsic problems: the central server has to perform intensive computation and communication with the vehicles in real-time, which can make such solutions infeasible for large regions with many vehicles; and driver privacy is not protected since the drivers have to share their location as well as the origins and destinations of their trips with the server, which may prevent the adoption of such solutions. To address these problems, a hybrid vehicular re-routing system is presented in this dissertation. The system off-loads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To make collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. The system is hybrid because it still uses a server to determine an accurate global view of the traffic. In addition, the user privacy is balanced with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy substantially, while the re-routing effectiveness is minimally impacted