D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Methods for performance evaluation of VBR video traffic models," IEEE/ACM Trans. Networking, vol. 2, pp. 176--180, Apr. 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....over a wide range of buffer sizes. 1 Introduction In recent years, the modeling of VBR video traffic attracted a lot of interest. A large variety of models based on different approaches can be found in the current teletrafficliterature: histogram models (e.g. 13] Markovian models (e.g. [9]) autoregressivemodels (e.g. 1] TES models (e.g. 10] and evenselfmodels (e.g. 4] In most cases, however, the papers focus on the modeling itself or on the application of the models in simulations. Due to the complex correlation structure of video traffic [11] there is a ....

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman. Methods for performance evaluation of VBR video traffic models. IEEE/ACM Transactions on Networking, 2(2):176-- 180, Apr. 1994.

....black box approaches typically treat the measurement as a time series. They focus on capturing the statistical characteristics (particularly autocorrelation and marginal distribution) of empirical data to model network traffic, based on various approaches such as Markov process, ARIMA, TES etc. [24, 33, 48, 42, 32, 18, 23, 32, 40]. Although being able to reproduce the measured traffic correctly, these approaches generally ignore the underlying network structure and hence provide little or no insight about the observed characteristics of measured traffic and its underlying causes. On the other hand, structural modeling, ....

D. Lucantoni, M. Neuts, and A. Reibman. Methods for performance evaluation of vbr video traffic models. IEEE/ACM Trans. Networking, 2:176--180, 1994.

....(UDP) 6] sequence number is used to inform the video decoder to perform error concealment. In addition to the two areas introduced, research areas such as video traffic modeling would not be relevant without the standards being defined. Prior work on video traffic modeling can be found in [7] [8], 9] T0] and [TT] This paper is organized as follows. In Section 2, we adopt the rate shaping technique to perform joint source channel coding. In Section 3, updating mixture of principle components is shown to perform very well in the error concealment application. We conclude this paper in ....

D.M. Lucantoni, M. F. Neuts, and A. R. Reibman, Method for Performance Evaluation of VBR Video Traffic Models, IEEE/ACM Transactions on Networking, 2(2), 176-180, April 1994

....overly throttling the compres sion rate. Modeling We first look at modeling as a means to describe a bit rate function to the network. There are several attempts to model the variable rate coded video bit stream R (t) in the literature, such as references [16] 22] 23] 25] 34] 42] 59] [65] [67] 70] 85] 90] 94] 96] Most of these studies combine the modeling with some queuing analysis to determine multiplexing performance. See comment on usefulness of this procedure on page 16. There is good evidence that this bit rate process exhibits long range behavior [4] 27] This ....

D. M. Lucantoni, et alii, "Methods for Per- formance Evaluation of VBR Video Traffic Mod- els," IEEE/ACM Transactions on Networking, Vol. 2, No. 2, April 1994, pp. 176-180.

....and imitates their effect on the video information. This model utilizes the results of both black box and white box analysis (see Figure 14) The synthetic traffic stream yielded by the model is verified by the Leaky Bucket Analysis method [5] which characterizes the burstiness of the VBR stream [15,16]. Black Box Analysis VBR Traffic N so rceutrafficchar H ie4 S i h. i ;i; tage, I Synthenc Traffic Traces . Figure 14 Concept of Model Building Technique and Verification 5.1 Model for Encoding This model takes a sequence of captured frames, i.e. Nf k, their probability mass function ....

....traffic traces. At the end of this section we briefly overview the applications of our hierarchical model. 6. 1 Leaky Bucket Analysis The first performance test is the Leaky Bucket Analysis (LBA) because it characterizes the burstiness of the traffic simultaneously at several time scales [5,15]. Moreover, this analysis directly provides the traffic characteristics that are actually tested at the Usage Parameter Control, i.e. the peak cell rate and cell delay variation tolerance as well as sustainable cell rate and burst tolerance parameters [13] That is we can see the traffic with the ....

D.M. Lucantoni, M. F. Neuts and A. R. Reibman, Methods for Performance Evaluation of VBR Video Traffic Models, IEEE/ACM Transactions on Networking, (Apr. 1994).

....even for large lags. Traffic models are analyzed based on goodness of fit, number of parameters needed to describe the model, parameter estimation, and analytical tractability. To evaluate goodness of fit, one needs to define metrics that determine how close the model is to the actual data [2]. These metrics have to be directly related to the performance measures that are needed to be predicted from the model. The goodness of fit used in this article is based on the ability of the model to capture marginal distributions, auto correlation structure, and ultimately predict delays and ....

D. Lucantoni, M. Neuts, and A. Reibman, "Methods for Performance Evaluation of VBR Video Traffic Models," IEEE/ACM Trans. Networking, vol. 2, Apr. 1994, pp. 176--80.

....changes explicitly. The model does rely 8 The frame level corresponds to MPEG or H.261 pictures. 9 #x# is the floor function, where x is rounded towards # on the use of a classical distribution (negative binomial) in DAR; however, the empirical distribution could be used. Lucantoni et al. [16] proposed a model using a discretestate, continuous time Markov renewal process (MRP) which they compared to the DAR model. This model is of the same vein as MMPP, but instead the bit rate is fixed, not probabilistic. They divided the range of possible rates into 40 equidistant levels and ....

....parameters (transition probabilities) Unfortunately, the drawback to selfsimilar models is the computational complexity involved Table 4. Summary of VBR model attributes Scene # Model type Parameters change Level B frames 1 ARMA [14] 1003 No subframe No 2 DAR a [15] 4 No Frame No 3 MRP [16] 40 No Frame No 4 AR MC [17] 8 Yes Frame No 5 MC [18] 51 102 No Frame Slice No 6 AR MC [19] 9 No Frame No 7 AR [20] 20 No Block No 8 DAR i.i.d [21] 10 Yes Frame No 9 DAR i.i.d. 22] 5 Yes Frame No 10 TES [26] 2K b 3 H c No GOB No 11 GTES [27] 5 H Yes Frame slice No 12 ....

[Article contains additional citation context not shown here]

Lucantoni DM, Neuts MF, Reibman A (1994) Methods for Performance Evaluation of VBR Video Traffic Models. IEEE/ACM Trans Networking 2(2): 176--180

....a deterministic service for a VBR video we need a deterministic, i.e. a worst case traffic characterization of the video stream. The novelty of our approach consists in using a so called burstiness function to characterize a video. The burstiness function was used by several authors (e.g. 4] [9], 8] 7] 11] as a deterministic characterization of VBR video streams. It can be seen as a set of leaky buckets to which the traffic conforms in a lossless way with leak rates ranging between the average and peak rate of the traffic of a video stream. More formally, given a VBR video traffic ....

D. Lucantoni, M. Neuts, and A. Reibman. Methods for Performance Evaluation of VBR Video Traffic Models. IEEE/ACM Transactions On Networking, 2(2):176--180, Apr. 1994.

....ability to test control methods using software tools (e.g. 14] and [15] reduces cost and development time. Unfortunately due to the stochastic nature of ATM traffic such simulation is difficult. Saito[16] enters into a discussion on the nature of ATM traffic as does Chang[17] Lucantoni et al.[18] published an entire paper reviewing VBR traffic models and carrying out evaluations of their performance. The complexity of the simulation is based on the fact that VBR traffic usually possesses a Probability Distribution Function (PDF) that is both unknown and open. This implies that making ....

M. F. Neuts D. M. Lucantoni and A. R. Reibman. Methods for performance evaluation of VBR video traffic models. IEEE/ACM Transactions on Networking, 2(2):176--180, April 1994.

....simple enough to enable estimation of performance parameters. As an example the ON OFF model discussed in Section 2.3.2 is a parametric model. It has the advantage that a closed form expression for its effective bandwidth is known (equation (2. 6) but from experiments [22] and real traffic studies [23] we know that such a model is not a realistic representation of real VBR video data. In Section 2.3.3.1 we discuss the research that has been carried out in this area whilst in Section 2.4 we concern ourselves with the work we carried out in adapting structured Markovian models for ATM. 2.3.3.1 ....

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Methods for performance evaluation of VBR video traffic models," IEEE/ACM Transactions on Networking, vol. 2, pp. 176--180, April 1994.

....(Y axis) for 2000 frame trace for 20 multiplexed sources using actual data and DAR(1) model tiplexer. Algorithm C was used in that study. This result was shown to hold for algorithms A and B in [HLT94] Further evidence that the DAR model is a good fit to video conference data is given in [LNR94] where the analyzed sequence was generated by a codec different from those used in our studies. This three parameter model is restricted to video conferences because we have not so far been able to adequately model entertainment video sources using similar models [HLA94] 5. ANALYTIC AND ....

D. Lucantoni, M. Neuts, A. Reibman "Methods for performance evaluation of VBR video traffic models", IEEE/ACM Transactions on Networking, 3(2), pp. 176--180, April 1994.

....different applications. In this paper, we study a new approach to support VBR video called REnegotiated Deterministic Variable Bit Rate or RED VBR service. We utilize two important properties of compressed video. First, compressed video traffic usually exhibits burstiness over multiple time scales [12, 24, 27]. At least two levels of burstiness are important for a resource allocation algorithm: burstiness on a shorter time scale due to the coding algorithm and small time scale variations in picture information content, and burstiness on a longer time scale due to scene changes. Correspondingly, ....

D. Lucantoni, M. Neuts, and A. Reibman. Methods for performance evaluation of VBR video traffic models. IEEE/ACM Transactions on Networking, 2(2):176--180, April 1994.

....over a wide range of buffer sizes. 1 Introduction In recent years, the modeling of VBR video traffic attracted a lot of interest. A large variety of models based on different approaches can be found in the current teletraffic literature: histogram models (e.g. 13] Markovian models (e.g. [9]) autoregressive models (e.g. 1] TES models (e.g. 10] and even self similar models (e.g. 4] In most cases, however, the papers focus on the modeling itself or on the application of the models in simulations. Due to the complex correlation structure of video traffic [11] there is a ....

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman. Methods for performance evaluation of VBR video traffic models. IEEE/ACM Transactions on Networking, 2(2):176-- 180, Apr. 1994.

....even for large lags. Traffic models are analyzed based on goodness of fit, number of parameters needed to describe the model, parameter estimation, and analytical tractability. To evaluate goodness of fit, one needs to define metrics that determine how close the model is to the actual data [2]. These metrics have to be directly related to the performance measures that are needed to be predicted from the model. The goodness of fit used in this paper is based on the ability of the model to capture marginal distributions, auto correlation structure, and ultimately predict delays and cell ....

D. Lucantoni, M. Neuts, and A. Reibman, "Methods for performance evaluation of VBR video traffic models," IEEE/ACM Transactions on Networking, Vol. 2, pp. 176--180, April 1994.

....prediction of the queue length distribution with LRD network traffic. We also tentatively use a multi queueing system to interpret the existence of LRD in the packet round trip delay process, which we believe is caused by LRD of Internet traffic. 1 Introduction In recent years, empirical studies [6, 10, 7] on network traffic both in local area networks (LAN) and wide area networks (WAN) convincingly show that the properties of actual traffic are very different from that predicted by traditional teletraffic models, such as Poisson process. For actually measured traffic, the correlation in traffic ....

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman. Methods for performance evaluation of vbr video traffic models. IEEE/ACM Transactions on Networking, 2(2):176--180, April 1994.

....into the behaviour of this model under a range of different parameter conditions. 1. INTRODUCTION The production of ATM traffic using Monte Carlo techniques has proved to be a difficult task due to the nature of the variable bit rate services that such networks are expected to provide (see, e. g,[1]) Markovian models have proved very popular with researchers as they provide a compromise between accuracy and complexity. Examples of the use of a variety of Markov models can be found in [2] 3] One difficulty that must be overcome when modelling ATM traffic is how to define it within a ....

....study of call admission control algorithms and their effect on the cell loss ratio. Hopefully the model will be able to validate results obtained by other methods such as large deviation theory [6] and it could be compared with other models using techniques such as those presented in Neuts et al.[1]. ....

M. F. Neuts D. M. Lucantoni and A. R. Reibman. Methods for performance evaluation of VBR video traffic models. IEEE/ACM Transactions on Networking, 2(2):176--180, April 1994.

....increase in buffer size. A similar insensitivity to buffer size for video teleconferencing is pointed out in [24] 3.2 Modeling two layer video Here, we adapt the single source model described in the previous section for VBR video to model the enhancement layer of a two layer video encoder. In [7] we present a model for one layer video that incorporates feedback for buffer control, as shown in Figure 3. This model enables characterization of a video source compressed for a constant rate channel. The source model generates the output bit rate of a constant quality VBR encoder with ....

....parameter fixed at Q=4, while a multiplicative scaling factor accounts for variations in bit rate caused by using a different quantization parameter to ensure no buffer overflow or underflow. The model in Figure 3 was shown to accurately capture the video quality when CBR transport is used [7]. We extend this concept to obtain a model for two layer video when the base layer is transported with CBR, as shown in Figure 4. The base and enhancement layer data were measured using a constant quantizer step size of Q b = 8 in the base layer and Q e = 3 in the enhancement layer. Since the ....

D.M. Lucantoni, M.F. Neuts and A.R. Reibman, "Methods for performance evaluation of VBR Video Traffic Models," IEEE/ACM Transac. on Networking, vol. 2, p176-180, 1994.

....birth death Markov model. Longer sequences of moderate activity videoconference data were modeled by Heyman et al. 6] In this model a first order discrete state autoregressive model (DAR) was augmented with a Markov chain structure to model the transitions between states. Lucantoni et al. [7] compared the DAR model to a Markov renewal process model (MRP) The MRP was shown to better capture the burstiness in the low activity video data, although it was still inadequate in matching the tail probabilities at large buffer sizes. Further, the DAR model has been found to be more favorable ....

.... fit within the declared traffic descriptors [32] In general, the coder that produces a bitstream conforming to constraints will not have the same statistical characteristics of an unconstrained coder [20] However, statistical models of unconstrained coders are still useful in a variety of roles [7]. In this paper, we use a model of an unconstrained VBR coder to evaluate the burstiness of the one and two layer coders, as well as to provide insight into both useful traffic descriptor parameter values and the resulting multiplexing gains if these traffic descriptor parameter values are used ....

[Article contains additional citation context not shown here]

D.M. Lucantoni, M.F. Neuts and A.R. Reibman, "Methods for performance evaluation of VBR Video Traffic Models," IEEE/ACM Transac. on Networking, vol. 2, p176-180, 1994.

No context found.

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Methods for performance evaluation of VBR video traffic models," IEEE/ACM Trans. Networking, vol. 2, pp. 176--180, Apr. 1994.

No context found.

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Method for Performance Evaluation of VBR Video Traffic Models", IEEE/ACM Transactions on Networking, 2(2), April 1994, pp. 176-180.

No context found.

D. Lucantoni, M. Neuts, and A. Reibman. Methods for performance evaluation of vbr video traffic models. IEEE/ACM Trans. Networking, 2:176--180, 1994.

No context found.

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Methods for Performance Evaluation of VBR Video Traffic Models", IEEE/ACM Transactions on Networking, Vol. 2, No. 2, pp. 176-180, February 1996. 145

No context found.

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Methods for performance evaluation of VBR video traffic models," IEEE/ACM Trans. Networking, vol. 2, pp. 176--180, Apr. 1994.

No context found.

D. M. Lucantoni, and M. F. Neuts, "Methods for Performance Evaluation of VBR Video Traffic Models," IEEE/ACM Transactions on Networking, vol. 2, no. 2, (April 1994): 176-180

No context found.

D. M. Lucantoni, M. F. Neuts, and A. R. Reibman, "Methods for performance evaluation of VBR video traffic models," IEEE/ACM Trans. Networking, vol. 2, no. 2, pp. 176-180 Apr. 1994.

First 50 documents

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC