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  • 1.
    Carlsson, Niklas
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Eager, Derek
    University of Saskatchewan, Canada.
    Krishnamoorthi, Vengatanathan
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Polishchuk, Tatiana
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Optimized Adaptive Streaming of Multi-video Stream Bundles2017In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 19, no 7, p. 1637-1653Article in journal (Refereed)
    Abstract [en]

    In contrast to traditional video, multi-view video streaming allows viewers to interactively switch among multiple perspectives provided by different cameras. One approach to achieve such a service is to encode the video from all of the cameras into a single stream, but this has the disadvantage that only a portion of the received video data will be used, namely that required for the selected view at each point in time. In this paper, we introduce the concept of a multi-video stream bundle that consists of multiple parallel video streams that are synchronized in time, each providing the video from a different camera capturing the same event or movie. For delivery we leverage the adaptive features and time-based chunking of HTTP-based adaptive streaming, but now employing adaptation in both content and rate. Users are able to change their viewpoint on-demand and the client player adapts the rate at which data are retrieved from each stream based on the users current view, the probabilities of switching to other views, and the users current bandwidth conditions. A crucial component of such a system is the prefetching policy. For this we present an optimization model as well as a simpler heuristic that can balance the playback quality and the probability of playback interruptions. After analytically and numerically characterizing the optimal solution, we present a prototype implementation and sample results. Our prefetching and buffer management solution is shown to provide close to seamless playback switching when there is sufficient bandwidth to prefetch the parallel streams.

  • 2.
    Carlsson, Niklas
    et al.
    University of Saskatchewan.
    Mahanti, Anirban
    IIT Delhi, India.
    Li, Zongpeng
    University of Calgary.
    Eager, Derek L.
    University of Saskatchewan.
    Optimized Periodic Broadcast of Non-linear Media2008In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, ISSN 1520-9210, Vol. 10, no 5, p. 871-884Article in journal (Refereed)
    Abstract [en]

    Conventional video consists of a single sequence of video frames. During a client's playback period, frames are viewed sequentially from some specified starting point. The fixed frame ordering of conventional video enables efficient scheduled broadcast delivery, as well as efficient near on-demand delivery to large numbers of concurrent clients through use of periodic broadcast protocols in which the video file is segmented and transmitted on multiple channels. This paper considers the problem of devising scalable protocols for near on-demand delivery of “nonlinear” media files whose content may have a tree or graph, rather than linear, structure. Such media allows personalization of the media playback according to individual client preferences. We formulate a mathematical model for determination of the optimal periodic broadcast protocol for nonlinear media with piecewise-linear structures. Our objective function allows differing weights to be placed on the startup delays required for differing paths through the media. Studying a number of simple nonlinear structures we provide insight into the characteristics of the optimal solution. For cases in which the cost of solving the optimization model is prohibitive, we propose and evaluate an efficient approximation algorithm.

  • 3.
    Henriquez, Pedro
    et al.
    University of Central Lancashire, England.
    Matuszewski, Bogdan J.
    University of Central Lancashire, England.
    Andreu-Cabedo, Yasmina
    University of Central Lancashire, England.
    Bastiani, Luca
    CNR, Italy.
    Colantonio, Sara
    CNR, Italy.
    Coppini, Giuseppe
    CNR, Italy.
    DAcunto, Mario
    CNR, Italy.
    Favilla, Riccardo
    CNR, Italy.
    Germanese, Danila
    CNR, Italy.
    Giorgi, Daniela
    CNR, Italy.
    Marraccini, Paolo
    CNR, Italy.
    Martinelli, Massimo
    CNR, Italy.
    Morales, Maria-Aurora
    CNR, Italy.
    Antonietta Pascali, Maria
    CNR, Italy.
    Righi, Marco
    CNR, Italy.
    Salvetti, Ovidio
    CNR, Italy.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Randeberg, Lise
    Norwegian University of Science and Technology, Norway.
    Bjorgan, Asgeir
    Norwegian University of Science and Technology, Norway.
    Giannakakis, Giorgos
    Fdn Research and Technology Hellas, Greece.
    Pediaditis, Matthew
    Fdn Research and Technology Hellas, Greece.
    Chiarugi, Franco
    Fdn Research and Technology Hellas, Greece.
    Christinaki, Eirini
    Fdn Research and Technology Hellas, Greece.
    Marias, Kostas
    Fdn Research and Technology Hellas, Greece.
    Tsiknakis, Manolis
    Fdn Research and Technology Hellas, Greece; Technology Educ Institute Crete, Greece.
    Mirror Mirror on the Wall ... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization2017In: IEEE transactions on multimedia, ISSN 1520-9210, E-ISSN 1941-0077, Vol. 19, no 7, p. 1467-1481Article in journal (Refereed)
    Abstract [en]

    A persons well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

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