Methods for performance evaluation of VBR video traffic models

Abstract-Models for predicting the performance of multiplexed variable bit rate video sources are important for engineering a network. However, models of a single source are also important for parameter negotiations and call admittance algorithms. In this paper we propose to model a single video source as a Markov renewal process whose states represent different bit rates. We also propose two novel goodness-of-fit metrics which are directly related to the specific performance aspects that we want to predict from the model. The first is a leaky bucket contour plot which can be used to quantify the burstiness of any traffic type. The second measure applies only to video traffic and measures how well the model can predict the compressed video quality. I. INTTtODUCTtON I T is well recognized that the viability of B-ISDN/ATM depends on the development of effective and implementable congestion control schemes. While many frameworks and techniques are under discussion (see, e.g., [l]), at least two capabilities have been agreed to as necessary in any framework that might arise.) The first is a comection admission control (CAC) by which the network will decide to accept or reject a new connection based on a set of agreed to traffic descriptors and on available resources. Once a connection is accepted, a second necessary control issome form of usage parameter control (UPC) which will insure that connections stay within their negotiated resource parameters. A popular UPC would involve a leaky bucket monitor of traffic entering the system, where traffic deemed as excessive by the monitor could either be dropped or tagged as low priority and allowed to proceed through the network to take advantage of potentially unused resources. Performance modeling is necessary to determine which techniques or set of techniques will be appropriate for eventual implementation in a B-ISDN network. Such models need to take into account traffic characteristics from realistic services that would be carried in a B-ISDN network. In particular, we need traffic models which will accurately represent the statistical nature of very high-speed, bursty services. Two classes of traffic models need to be developed: multiplexed source models and single source models. Although the same traffic model might be used in both cases, some models might be more suitable for one than the other. Multiplexed models will capture the effects of statistically multiplexing bursty sources and will predict to what extent the superposition of bursty streams is "smoothed". These models will be useful in traffic engineering the network (e.g., deciding how many links or virtual paths to put between different locations) and in traffic management (e.g., designing connection admission control algorithms, etc.) Several models have already been proposed in this direction (see, e.g., There are several areas where single source models are useful. They could be used to study what types of traffic descriptors make sense for parameter negotiation with the network at call setup. For example, if leaky bucket monitoring is used as a traffic descriptor, the negotiation might consist of the source specifying what parameters could be used in the leaky bucket for a given connection. Single source models can help in the selection of these parameters. Also, some applications may do some end-to-end rate control to ensure that minimal traffic is lost during periods of network congestion. Source models could be used in testing various rate control algorithms, Finally, these models are also useful in predicting the qualityof-service (QOS) that a particular application might experience during different levels of congestion. In deriving traffic models, we need metrics which can determine how "close" the model is to the actual traffic. Standard statistical measures such as means, variances, and other goodness-of-fit tests may not be appropriate here since they may not be measuring the characteristics of the process that are most important for either predicting the effect of the source on the resources in the network or the performance the source will experience. Instead, the goodness-of-fit metrics need to be directly related to the specific aspects of performance that we want to predict from the model; see e.g., [6]. In this paper, we propose two criteria for judging the appropriateness of a traffic model for bursty services. The first one applies to any high speed bursty data service and the second is specific to a variable-bit-rate (VBR) video application. To illustrate these measures we compare a previous model of VBR video with a new model proposed here. II. MODELING VARIABLE-BIT-RATE VIDEO The data we are modeling was recorded at an actual teleconference meeting. Each scene depicts the head and shoulders of one person, and is 5 rein, or 9000 frames, long. Since each 5 min of video required approximately one week to encode using software, the motivation for developing accurate models with a low computational burden is clear. A typical 10634692i94$04.0

Flow Level QoE of Video Streaming in Wireless Networks

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.Comment: 14 page

A genetic approach to Markovian characterisation of H.264 scalable video

We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios

A Utility-based QoS Model for Emerging Multimedia Applications

Existing network QoS models do not sufficiently reflect the challenges faced by high-throughput, always-on, inelastic multimedia applications. In this paper, a utility-based QoS model is proposed as a user layer extension to existing communication QoS models to better assess the requirements of multimedia applications and manage the QoS provisioning of multimedia flows. Network impairment utility functions are derived from user experiments and combined to application utility functions to evaluate the application quality. Simulation is used to demonstrate the validity of the proposed QoS model

Markovian Characterisation of H.264/SVC scalable video

In this paper, a multivariate Markovian traffic: model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture. both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios

Video traffic modeling and delivery

Video is becoming a major component of the network traffic, and thus there has been a great interest to model video traffic. It is known that video traffic possesses short range dependence (SRD) and long range dependence (LRD) properties, which can drastically affect network performance. By decomposing a video sequence into three parts, according to its motion activity, Markov-modulated self-similar process model is first proposed to capture autocorrelation function (ACF) characteristics of MPEG video traffic. Furthermore, generalized Beta distribution is proposed to model the probability density functions (PDFs) of MPEG video traffic. It is observed that the ACF of MPEG video traffic fluctuates around three envelopes, reflecting the fact that different coding methods reduce the data dependency by different amount. This observation has led to a more accurate model, structurally modulated self-similar process model, which captures the ACF of the traffic, both SRD and LRD, by exploiting the MPEG structure. This model is subsequently simplified by simply modulating three self-similar processes, resulting in a much simpler model having the same accuracy as the structurally modulated self-similar process model. To justify the validity of the proposed models for video transmission, the cell loss ratios (CLRs) of a server with a limited buffer size driven by the empirical trace are compared to those driven by the proposed models. The differences are within one order, which are hardly achievable by other models, even for the case of JPEG video traffic. In the second part of this dissertation, two dynamic bandwidth allocation algorithms are proposed for pre-recorded and real-time video delivery, respectively. One is based on scene change identification, and the other is based on frame differences. The proposed algorithms can increase the bandwidth utilization by a factor of two to five, as compared to the constant bit rate (CBR) service using peak rate assignment

Analysis Framework for Opportunistic Spectrum OFDMA and its Application to the IEEE 802.22 Standard

We present an analytical model that enables throughput evaluation of Opportunistic Spectrum Orthogonal Frequency Division Multiple Access (OS-OFDMA) networks. The core feature of the model, based on a discrete time Markov chain, is the consideration of different channel and subchannel allocation strategies under different Primary and Secondary user types, traffic and priority levels. The analytical model also assesses the impact of different spectrum sensing strategies on the throughput of OS-OFDMA network. The analysis applies to the IEEE 802.22 standard, to evaluate the impact of two-stage spectrum sensing strategy and varying temporal activity of wireless microphones on the IEEE 802.22 throughput. Our study suggests that OS-OFDMA with subchannel notching and channel bonding could provide almost ten times higher throughput compared with the design without those options, when the activity and density of wireless microphones is very high. Furthermore, we confirm that OS-OFDMA implementation without subchannel notching, used in the IEEE 802.22, is able to support real-time and non-real-time quality of service classes, provided that wireless microphones temporal activity is moderate (with approximately one wireless microphone per 3,000 inhabitants with light urban population density and short duty cycles). Finally, two-stage spectrum sensing option improves OS-OFDMA throughput, provided that the length of spectrum sensing at every stage is optimized using our model