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Breast cancer screening policies attempt to achieve timely diagnosis by regularly screening healthy women via various imaging tests. Various clinical decisions are needed to manage the screening process: selecting initial screening tests, interpreting test results, and deciding if further diagnostic tests are required. Current screening policies are guided by clinical practice guidelines (CPGs), which represent a 'one-size-fits-all' approach, designed to work well (on average) for a population, and can only offer coarse expert-based patient stratification that is not rigorously validated through data. Since the risks and benefits of screening tests are functions of each patient's features, personalized screening policies tailored to the features of individuals are desirable. To address this issue, we developed ConfidentCare: a computer-aided clinical decision support system that learns a personalized screening policy from electronic health record (EHR) data. By a 'personalized screening policy,' we mean a clustering of women's features, and a set of customized screening guidelines for each cluster. ConfidentCare operates by computing clusters of patients with similar features, then learning the 'best' screening procedure for each cluster using a supervised learning algorithm. The algorithm ensures that the learned screening policy satisfies a predefined accuracy requirement with a high level of confidence for every cluster. By applying ConfidentCare to real-world data, we show that it outperforms the current CPGs in terms of cost efficiency and false positive rates: a reduction of 31% in the false positive rate can be achieved.

In this paper, we study the system-level computational resource allocation problem among multiple multimedia tasks. We consider the multimedia tasks to be autonomous, i.e., they are selfish and behave strategically. We propose a resource allocation framework based on mechanism design to prevent the tasks from behaving strategically and manipulating the available system resources. We apply two mechanisms in the framework and assess their advantages over proportional-share resource allocation algorithms, which are often used in multimedia systems. We show in the simulations that the incorporation of mechanism design for system resource allocation is a promising solution that achieves efficient, fair and robust allocation against manipulation from strategic applications. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a dynamic channel-selection solution for autonomous wireless users transmitting delay-sensitive multimedia applications over cognitive radio networks. Unlike prior works that seldom consider the requirement of the application layer, our solution explicitly considers various rate requirements and delay deadlines of heterogeneous multimedia users. Note that the users usually possess private utility functions, application requirements, and distinct channel conditions in different frequency channels. To efficiently manage available spectrum resources in a decentralized manner, information exchange among users is necessary. Hence, we propose a novel priority virtual queue interface that determines the required information exchanges and evaluates the expected delays experienced by various priority traffics. Such expected delays are important for multimedia users due to their delay-sensitivity nature. Based on the exchanged information, the interface evaluates the expected delays using priority queuing analysis that considers the wireless environment, traffic characteristics, and the competing users' behaviors in the same frequency channel. We propose a dynamic strategy learning (DSL) algorithm deployed at each user that exploits the expected delay and dynamically adapts the channel selection strategies to maximize the user's utility function. We simulate multiple video users sharing the cognitive radio network and show that our proposed solution significantly reduces the packet loss rate and outperforms the conventional single-channel dynamic resource allocation by almost 2 dB in terms of video quality. [ABSTRACT FROM PUBLISHER]

Available resources in peer-to-peer (P2P) systems depend strongly on resource contributions made by individual peers. Empirical data shows that in the absence of incentives, a majority of the participating peers do not contribute resources. Modeling interactions between individual peers is often difficult as the number of peers in the system can be very large, and the relationships among them can be very complex. In this paper, we propose a new solution for P2P systems, where peers upload and download content to and from the contributing peers based on agreed-upon/determined sharing rates. We propose a P2P solution that deters free-riders by imposing constraints on participating peers; specifically, a peer is allowed access to new content only as long as its own content contribution exceeds an adaptively set threshold. The constraints are enforced either by a central authority (e.g., a tracker) or by a decentralized coalition of peers in a swarm, social network, etc. We derive optimal upload policies for the peers given their estimated future download requirements and their previous contribution (credit) to the other peers. Our results show considerable improvement in the cost-benefit tradeoff for peers that deploy such an optimal policy as compared to heuristic upload policies. We also propose mechanisms based on which the coalition of peers can provide incentives or penalties to participating peers to adjust their policies such that the availability of content and/or number of peers contributing content is maximized. [ABSTRACT FROM PUBLISHER]

Various packet scheduling, dynamic routing, error-protection, and channel adaptation strategies have been proposed at different layers of the protocol stack to address multi-user video streaming over multihop wireless networks. However, these cross-layer transmission strategies can be efficiently optimized only if they use accurate information about the network conditions and hence, are able to timely adapt to network changes. Due to the informationally decentralized nature of the multihop wireless network, performing centralized optimization for delay-sensitive video streaming application based on global information about the network status is not practical. Distributed solutions that adapt the transmission strategies based on timely information feedback need to be considered. To acquire this information feedback for cross-layer adaptation, we deploy an overlay infrastructure, which is able to relay the necessary information about the network status and incurred delays across different network ldquohorizonsrdquo (i.e., across a different number of hops in a predetermined period of time). In this paper, we propose a distributed streaming approach that is optimized based on the local information feedback acquired from the various network horizons. We investigate the distributed cross-layer adaptation at each wireless node by considering the advantages resulting from an accurate and frequent network information feedback from larger horizons as well as the drawbacks resulting from an increased transmission overhead. Based on the information feedback, we can estimate the risk that packets from different priority classes will not arrive at their destination before their decoding deadline expires. Subsequently, the various transmission strategies such as packet scheduling, retransmission limit and dynamic routing policies are adapted to jointly consider the estimated risk as well as the impact in terms of distortion of the different priority classes. Our results show that- the proposed dynamic routing policy based on timely information feedback outperforms existing state-of-the-art on-demand routing solutions by more than 2 dB in terms of the received video quality. [ABSTRACT FROM PUBLISHER]

Existing wireless networks provide dynamically varying resources with only limited support for the quality of service required by the bandwidth-intense, loss-tolerant and delay-sensitive multimedia applications. This variability of resources does not significantly impact delay insensitive data transmission (e.g., file transfers), but has considerable consequences for multimedia applications. Recently, the research focus has been to adapt existing algorithms and protocols at the lower layers of the protocol stack to better support multimedia transmission applications and conversely, to modify application layer solutions to cope with the varying wireless networks resources. In this paper, we show that significant improvements in wireless multimedia performance can be obtained by deploying a joint application-layer adaptive packetization and prioritized scheduling and MAC-layer retransmission strategy. We deploy a state-of-the-art wavelet coder for the compression of the video data that enables on-the-fly adaptation to changing channel conditions and inherent prioritization of the video bitstream. We pose the cross-layer problem as a distortion minimization given delay constraints and derive analytical solutions by modifying existing joint source-channel coding theory aimed at fulfilling rate, rather than delay, constraints. We also propose real-time algorithms that explicitly consider the available information about previously transmitted packets. The obtained results show significant improvements in terms of video quality as opposed to ad-hoc optimizations currently deployed, while the complexity associated with performing this optimization in real time, i.e., at transmission time, is limited [ABSTRACT FROM PUBLISHER]

A plethora of coding and streaming mechanisms have been proposed for real-time multimedia transmission over the Internet. However, most proposed mechanisms rely only on global (e.g. based on end-to-end measurements), delayed (at least by the round-trip-time), or statistical (often based on simplistic network models) information available about the network state. Based on recently-proposed state-of-the-art open-loop video coding schemes, we propose a new integrated streaming and routing framework for robust and efficient video transmission over networks exhibiting path failures. Our approach explicitly takes into account the network dynamics, path diversity, and the modeled video distortion at the receiver side to optimize the packet redundancy and scheduling. In the derived framework, multimedia streams can be adapted dynamically at the video server based on instantaneous routing-layer information or failure-modeling statistics. The performance of our integrated application and network-layer method is simulated against equivalent approaches that are not optimized based on routing-layer feedback and distortion modeling, and the obtained gains in video quality are quantified [ABSTRACT FROM PUBLISHER]

Robust streaming of video over 802.11 wireless LANs (WLANs) poses many challenges, including coping with packets losses caused by network buffer overflow or link erasures. In this paper, we propose a novel error protection method that can provide adaptive quality-of-service (QoS) to layered coded video by utilizing priority queueing at the network layer and retry-limit adaptation at the link layer. The design of our method is motivated by the observation that the retry limit settings of the MAC layer can be optimized in such a way that the overall packet losses that are caused by either link erasure or buffer overflow are minimized. We developed a real-time retry limit adaptation algorithm to trace the optimal retry limit for both the single-queue (or single-layer) and multiqueue (or multilayer) cases. The video layers are unequally protected over the wireless link by the MAC with different retry limits. In our proposed transmission framework, these retry limits are dynamically adapted depending on the wireless channel conditions and traffic characteristics. Furthermore, the proposed priority queueing discipline is enhanced with packet filtering and purging functionalities that can significantly save bandwidth by discarding obsolete or un-decodable packets from the buffer. Simulations show that the proposed cross-layer protection mechanism can significantly improve the received video quality. [ABSTRACT FROM PUBLISHER]

Real-time streaming of audiovisual content over the Internet is emerging as an important technology area in multimedia communications. Due to the wide variation of available bandwidth over Internet sessions, there is a need for scalable video coding methods and (corresponding) flexible streaming approaches that are capable of adapting to changing network conditions in real time. In this paper, we describe a new scalable video-coding framework that has been adopted recently by the MPEG-4 video standard. This new MPEG-4 video approach, which is known as Fine-Granular-Scalability (FGS), consists of a rich set of video coding tools that support quality (i.e., SNR), temporal, and hybrid temporal-SNR scalabilities. Moreover, one of the desired features of the MPEG-J FGS method is its simplicity and flexibility in supporting unicast and multicast streaming applications over IF [ABSTRACT FROM PUBLISHER]