Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

High Quality of Service on Video Streaming in P2P Networks using FST-MDC

Video streaming applications have newly attracted a large number of participants in a distribution network. Traditional client-server based video streaming solutions sustain precious bandwidth provision rate on the server. Recently, several P2P streaming systems have been organized to provide on-demand and live video streaming services on the wireless network at reduced server cost. Peer-to-Peer (P2P) computing is a new pattern to construct disseminated network applications. Typical error control techniques are not very well matched and on the other hand error prone channels has increased greatly for video transmission e.g., over wireless networks and IP. These two facts united together provided the essential motivation for the development of a new set of techniques (error concealment) capable of dealing with transmission errors in video systems. In this paper, we propose an flexible multiple description coding method named as Flexible Spatial-Temporal (FST) which improves error resilience in the sense of frame loss possibilities over independent paths. It introduces combination of both spatial and temporal concealment technique at the receiver and to conceal the lost frames more effectively. Experimental results show that, proposed approach attains reasonable quality of video performance over P2P wireless network.Comment: 11 pages, 8 figures, journa

Peer-to-peer stream merging for stored multimedia

In recent years, with the fast development of resource capability of both the Internet and personal computers, multimedia applications like video-on-demand (VOD) streaming have gained dramatic growth and been shown to be potential killer applications in the current and next-generation Internet. Scalable deployment of these applications has become a hot problem area due to the potentially high server and network bandwidth required in these systems.The conventional approach in a VOD streaming system dedicates a media stream for each client request, which is not scalable in a wide-area delivery system serving potentially very large numbers of clients. Recently, various efficient delivery techniques have been proposed to improve the scalability of VOD delivery systems. One approach is to use a scalable delivery protocol based on multicast, such as periodic broadcast or stream merging. These protocols have been mostly developed for single-server based systems and attempt to have each media stream serve as many clients as possible, so as to minimize the required server and network bandwidth. However, the performance improvements possible with techniques that deliver all streams from a single server are limited, especially regarding the required network bandwidth. Another approach is based on proxy caching and content replication, such as in content delivery networks (CDN). Although this approach is able to effectively distribute load across multiple CDN servers, the cost of this approach may be high.With the focus on further improving the system efficiency regarding the server and network bandwidth requirement, a new scalable streaming protocol is developed in this work. It adapts a previously proposed technique called hierarchical multicast stream merging (HMSM) to use a peer-to-peer delivery approach. To be more efficient in media delivery, the conventional early merging policy associated with HMSM is extended to be compatible with the peer-to-peer environment, and various peer selection policies are designed for initiation of media streams. The impact of limited peer resource capability is also studied in this work. In the performance study, a number of simulation experiments are conducted to evaluate the performance of the new protocol and various design policies, and promising results are reported

Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-free Transmission

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold. First, to systematically investigate the benefits of UEP+RLNC layered approach in servicing users with different reception capabilities. Second, to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study `expected percentage of decoded frames' as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.Comment: 15 pages, 12 figures, 3 tables, Under 2nd round of review, IEEE Transactions on Communication