Performance Evaluation of ATM Networks Carrying Constant and Variable Bit-Rate Video Traffic

In this paper, we present the performance of asynchronous transfer mode (ATM) networks supporting audio/video traffic. The performance evaluation is done by means of a computer simulation model driven by real video traffic generated by encoding video sequences. We examine the glitching that occurs when the video information is not delivered on time at the receiver; we characterize various glitching quantities, such as the glitch duration, total number of macroblocks unavailable per glitch, and the maximum unavailable area per glitch. For various types of video contents, we compare the maximum number of constant bit-rate (CBR) and constant-quality variable bit-rate (CQ-VBR) video streams that can be supported by the network while meeting the same end-to-end delay constraint, the same level of encoded video quality, and the same glitch rate constraint. We show that when the video content is highly variable, many more CQ-VBR streams than CBR streams can be supported under given quality and delay constraints, while for relatively uniform video contents (as in a videoconferencing session), the number of CBR and CQ-VBR streams supportable is about the same. We also compare the results with those obtained for a 100Base-T Ethernet segment. We then consider heterogeneous traffic scenarios, and show that when video streams with different content, encoding scheme, and encoder control schemes are mixed, the results are at intermediate points compared to the homogeneous cases, and the maximum number of supportable streams of a given type can be determined in the presence of other types of video traffic by considering an "effective bandwidth" for each of the stream types. We consider multihop ATM network scenarios as well, and show that the number of video streams that can be supported on a given network node is very weakly dependent on the number of hops that the video streams traverse. Finally, we also consider scenarios with mixtures of video streams and bursty traffic, and determine the effect of bursty traffic load and burst size on the video performance.