The Experts below are selected from a list of 40578 Experts worldwide ranked by ideXlab platform

Rami J. Haddad - One of the best experts on this subject based on the ideXlab platform.

  • Nonlinear auto-regressive neural network model for forecasting Hi-Def H.265 Video traffic over Ethernet Passive Optical Networks
    SoutheastCon 2017, 2017
    Co-Authors: Collin J. Daly, David L. Moore, Rami J. Haddad
    Abstract:

    Video Bandwidth forecasting can help optimize the transmission of Video traffic over optical access networks. In this paper, we propose the use of a nonlinear auto-regressive (NAR) neural network model for forecasting H.265 Video Bandwidth requirements to optimize Video transmission within Ethernet Passive Optical Networks (EPONs). The Video's constituent I, P, and B frames are forecast separately to improve model forecasting accuracy. The proposed forecasting model is able to forecast H.265 encoded High-Definition Videos with an accuracy exceeding 90%. In addition, using the Video Bandwidth requirement predictions as grant requests within EPONs improved the efficiency of dynamic Bandwidth allocation (DBA). The use of nonlinear auto-regressive neural network grant sizing predictions within EPONs reduced the Video packet queueing delay significantly when the network was saturated near capacity.

  • Video Bandwidth Forecasting
    IEEE Communications Surveys & Tutorials, 2013
    Co-Authors: Rami J. Haddad, Michael P. Mcgarry, Patrick Seeling
    Abstract:

    We survey twenty years of research literature on Video frame size forecasting. We organize the discussion of the literature using model type and model parameters as a taxonomy. We discuss how to use Video frame size forecasts to forecast Video Bandwidth requirements. We provide extensive comparisons of forecast accuracy among the various mechanisms using data extracted from the literature and a set of common experiments we conducted. Lastly, we summarize our findings with respect to forecast accuracy and we identify open areas for research.

  • ICUMT - A new approach to Video Bandwidth prediction
    2009 International Conference on Ultra Modern Telecommunications & Workshops, 2009
    Co-Authors: Michael P. Mcgarry, Rami J. Haddad, John Mcalarney
    Abstract:

    We discuss a fundamentally new approach to digital packetized Video Bandwidth prediction (i.e., Bandwidth forecasting). This fundamentally new approach provides an accurate Bandwidth forecast at the expense of increased queueing delay. We outline this new forecasting method we call feedforward Bandwidth indication (FFBI), compare it to existing forecasting methods, and analyze the tradeoffs associated with its use. Finally, we outline future avenues of research.

Michael P. Mcgarry - One of the best experts on this subject based on the ideXlab platform.

  • Video Bandwidth Forecasting
    IEEE Communications Surveys & Tutorials, 2013
    Co-Authors: Rami J. Haddad, Michael P. Mcgarry, Patrick Seeling
    Abstract:

    We survey twenty years of research literature on Video frame size forecasting. We organize the discussion of the literature using model type and model parameters as a taxonomy. We discuss how to use Video frame size forecasts to forecast Video Bandwidth requirements. We provide extensive comparisons of forecast accuracy among the various mechanisms using data extracted from the literature and a set of common experiments we conducted. Lastly, we summarize our findings with respect to forecast accuracy and we identify open areas for research.

  • ICUMT - A new approach to Video Bandwidth prediction
    2009 International Conference on Ultra Modern Telecommunications & Workshops, 2009
    Co-Authors: Michael P. Mcgarry, Rami J. Haddad, John Mcalarney
    Abstract:

    We discuss a fundamentally new approach to digital packetized Video Bandwidth prediction (i.e., Bandwidth forecasting). This fundamentally new approach provides an accurate Bandwidth forecast at the expense of increased queueing delay. We outline this new forecasting method we call feedforward Bandwidth indication (FFBI), compare it to existing forecasting methods, and analyze the tradeoffs associated with its use. Finally, we outline future avenues of research.

Patrick Seeling - One of the best experts on this subject based on the ideXlab platform.

  • Video Bandwidth Forecasting
    IEEE Communications Surveys & Tutorials, 2013
    Co-Authors: Rami J. Haddad, Michael P. Mcgarry, Patrick Seeling
    Abstract:

    We survey twenty years of research literature on Video frame size forecasting. We organize the discussion of the literature using model type and model parameters as a taxonomy. We discuss how to use Video frame size forecasts to forecast Video Bandwidth requirements. We provide extensive comparisons of forecast accuracy among the various mechanisms using data extracted from the literature and a set of common experiments we conducted. Lastly, we summarize our findings with respect to forecast accuracy and we identify open areas for research.

E.g.t. Jaspers - One of the best experts on this subject based on the ideXlab platform.

  • Compression for reduction of off-chip Video Bandwidth
    Media Processors 2002, 2001
    Co-Authors: E.g.t. Jaspers
    Abstract:

    The architecture for block-based Video applications (e.g. MPEG/JPEG coding, graphics rendering) is usually based on a processor engine, connected to an external background SDRAM memory where reference images and data are stored. In this paper, we reduce the required memory Bandwidth for MPEG coding up to 67% by identifying the optimal block configuration and applying embedded data compression up to a factor four. It is shown that independent compression of fixed-sized data blocks with a fixed compression ratio can decrease the memory Bandwidth for a limited set of compression factors only. To achieve this result, we exploit the statistical properties of the burst-oriented data exchange to memory. It has been found that embedded compression is particularly attractive for Bandwidth reduction when a compression ratio 2 or 4 is chosen. This moderate compression factor can be obtained with a low-cost compression scheme such as DPCM with a small acceptable loss of quality.

Polychronis Koutsakis - One of the best experts on this subject based on the ideXlab platform.

  • H.264 and H.265 Video Bandwidth Prediction
    IEEE Transactions on Multimedia, 2018
    Co-Authors: Athina Kalampogia, Polychronis Koutsakis
    Abstract:

    The explosive growth of multimedia applications renders the efficiency of network resource allocation a problem of major importance. The burstiness of Video traffic, in particular, calls for traffic control solutions that will help prevent significant packet losses. Such losses can lead to unacceptable quality of service (QoS) and quality of experience (QoE) to users. In this paper, we focus on a large variety of H.264- and H.265-encoded Video traces with different GoP patterns. Different versions of each trace, in low, medium, and high quality have been used in our study. We evaluate the accuracy of an existing Video traffic prediction approach for the size of B-frames, and we propose a new Markovian model that predicts B-frames’ sizes with significantly higher accuracy. B-frame size prediction can be used in order to reduce Bandwidth requirements and smooth the encoded Video stream, by selective B-frame dropping, when the model predicts larger upcoming B-frame traffic than the network can handle.

  • INFOCOM Workshops - Using simulated annealing for improved Video Bandwidth prediction
    2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2017
    Co-Authors: Athina Kalampogia, Polychronis Koutsakis
    Abstract:

    The strain imposed by the Bandwidth demands of multimedia applications on wired and wireless networks calls for efficient novel solutions to the problem of network resource allocation, to avoid significant packet losses. In this letter, we focus on a large variety of MPEG-4, H.264 and H.265-encoded Video traces. We use the metaheuristic technique of Simulated Annealing to predict the size of B-frames, and compare it against an existing approach from the literature. B-frame size prediction can be used in order to reduce Bandwidth requirements and smoothen the encoded Video stream, by selective B-frame dropping. We show that Simulated Annealing can significantly improve the prediction accuracy.