Fundamental bounds and approximations for ATM multiplexers with applications to video teleconferencing

Elwalid, A.; Heyman, Daniel P.; Lakshman, T. V.; Mitra, Debasis; Weiss, Alan

doi:10.1109/49.400656

Cited by 197 publications

(136 citation statements)

References 25 publications

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“…However, in the same study, the authors observed that a simple DAR(1) model, based on a discrete-time, discrete state Markov Chain performs betterwith respect to queueing-than a simple AR(2) model. The results of this study are further verified by similar studies of videoconference traffic modelling and VBR video performance and simulation [6,12]. The above studies certainly constitute a valuable body of knowledge.…”

Section: Introductionsupporting

confidence: 67%

Estimation of Modelling Parameters for H.263-Quantized Video Traces

Drigas

Kouremenos²

2013

Journal of Computer Networks and Communications

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We propose methods for selecting the modelling parameters of H.263-quantized video traffic under two different encoding scenarios. For videos encoded with a constant quantization step (unconstrained), we conclude that a two-parameter power relation holds between the exhibited video bit rate and the quantizer value and that the autocorrelation decay rate remains constant for all cases. On the basis of these results, we propose a generic method for estimating the modelling parameters of unconstrained traffic by means of measuring the statistics of the single "raw" video trace. For rate-controlled video (constrained), we propose an approximate method based on the adjustment of the "shape" parameter of the counterpart-with respect to rate-unconstrained video trace. The convergence of the constructed models is assessed via q-q plots and queuing simulations. On the assumption that the popular MPEG-4 encoders like XVID, DIVX usually employ identical H.263 quantization and rate control schemes, it is expected that the results of this paper also hold for the MPEG-4 part 2 family.

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Section: Introductionsupporting

confidence: 67%

Estimation of Modelling Parameters for H.263-Quantized Video Traces

Drigas

Kouremenos²

2013

Journal of Computer Networks and Communications

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“…However, in the same study, the authors observed that a simple DAR(1) model, based on a discrete-time, discrete state Markov Chain performs better-with respect to queueing-than a simple AR(2) model. e results of this study are further veri�ed by similar studies of videoconference traffic modelling [7] and VBR video performance and simulation [6,10]. In [11], Dr. Heyman proposed and evaluated the GBAR process, as an accurate and well-performed single-source videoconference traffic model.…”

Section: Introductionmentioning

confidence: 63%

Characteristics of Video Traffic from Videoconference Applications: From H.261 to H.264

Domoxoudis

Kouremenos

Loumos

et al. 2013

Journal of Computer Networks and Communications

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is paper presents modelling results for H.26x video traffic generated by popular videoconference soware applications. e analysis of videoconference data, that were measured during realistic point-point videoconference sessions, led us to the general conclusion that the traffic can be distinguished into two categories: unconstrained and constrained. In the unconstrained traffic, there is a direct relation between the encoder and the form of the frequency histogram of the frame-size sequence. Moreover, for this type of traffic, strong correlations between successive video frames can be found. On the other hand, where bandwidth constraints are imposed during the encoding process, the generated traffic appears to exhibit similar characteristics for all the examined encoders with the very low autocorrelation values being the most notable one. On the basis of these results, this study proposes methods to calculate the parameters of a widely adopted autoregressive model for both types of traffic.

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“…The training data set is used to construct the reference models, whereas the test data set contains the raw home load measurements that are used in the validation process. We use our reference models to compute the transformer size required to achieve a loss of load probability of 2.74 × 10 −4 , which is the standard used in industry, using teletraffic theory [7], as described in [2]. Then, using this same transformer size, we run a numerical simulation using the raw measurements as the input and compute the total duration of overload, which is a numerical estimate of the loss of load probability.…”

Section: Validationmentioning

confidence: 99%

Markovian models for home electricity consumption

Ardakanian

Keshav

Rosenberg

2011

Proceedings of the 2nd ACM SIGCOMM Workshop on Green Networking

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Modelling home energy consumption is necessary for studying demand-response, transformer sizing, and distribution network simulation. Using an existing classification, we propose parsimonious Markovian reference models of home load for each class. We derive models for on-peak periods, offpeak periods, and mid-peak periods. These models are derived using traces based on fine-grained measurements of electricity consumption in 20 homes over four months. We validate the representativeness of our models in a specific application.

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Fundamental bounds and approximations for ATM multiplexers with applications to video teleconferencing

Cited by 197 publications

References 25 publications

Estimation of Modelling Parameters for H.263-Quantized Video Traces

Estimation of Modelling Parameters for H.263-Quantized Video Traces

Characteristics of Video Traffic from Videoconference Applications: From H.261 to H.264

Markovian models for home electricity consumption

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