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Compressed Video Communications Abdul Sadka
Copyright © 2002 John Wiley & Sons Ltd ISBNs: 0-470-84312-8 (Hardback); 0-470-84671-2 (Electronic)
Flow Control in Compressed Video Communications
In multimedia communications, compressed video streams need to be transmitted over networks that have inconsistent and time-varying bandwidth requirements. To make the best use of available network resources at any time and guarantee a maximum level of perceptual video quality from the end-user’s perspective, a certain flow control mechanism must be introduced into the video communication system (Cote et al., 1998; Wang, 2000). Over-rating the output of a video coder can cause an undesirable traffic explosion and lead to congested networks. On the other hand, uncontrolled reduction of the output bit rate of a video coder leads to unnecessary quality degradation and inefficient use of available bandwidth resources. Flow control techniques must then be employed to regulate and control the output bit rates of video sources in the network to achieve the best trade-off between quality and bandwidth utilisation (Girod, 1993).
One of the main challenges of video communications is to provide a guaranteed quality of service when the network is swamped with excessive delays and information loss rates (Kurose, 1993). Network congestion could be avoided by using preventive instead of reactive remedies. Congestion avoidance techniques in video communications must consist of an efficient flow control mechanism that regulates the rates of active video sources (Jacobson, 1988). In a bit rate regulation scheme, the video source might sometimes be required to decrease its output flow due to high traffic load across the network. This reduction in bit rate could certainly lead to quality degradation since the quantisation distortion becomes more noticeable at lower bit rates. However, the quality degradation resulting from a coarser quantisation process is far less detrimental to the video quality than the effect of intolerable time delays and high data loss rates caused by a state of network congestion. Network congestion effects could also be more disastrous in real-time video services where the decoded video quality is much less tolerant to delay and data loss. Therefore, some policy must be adopted to prevent the
FLOW CONTROL IN COMPRESSED VIDEO COMMUNICATIONS
occurrence of congestion or reduce its effect in high traffic load conditions. A lot of research efforts have been exerted to establish efficient techniques for resolving congestion. Bolot and Turletti (1994) have developed a feedback control mechanism for flow control of video sources over the multicast backbone (Kumar, 1996) of the Internet. In this preventive rate control scheme, the rate control of a video encoder is regulated by modifying some encoding parameters, as indicated by some feedback messages sent by network receivers. Each receiver sends a feedback message that includes some statistics data such as average packet transit time, average loss rate for multicast traffic, average packet delay, etc. The sender collates this data and adjusts its output flow accordingly. Another feedback mechanism (Bolot, Turletti and Wakeman, 1994) employs a probing technique to solicit information and estimate the number of receivers in a multicast tree. A number of video scaleability paradigms (Radha et al., 1999; Stuhlmuller, Link and Girod, 1999; Horn and Girod, 1997) have been proposed for Internet streaming applications. Other research efforts produced reactive approaches such as error concealment and video data recovery schemes, which we will elaborate on in the next chapter. In this chapter, we present a variety of rate control algorithms that can be used in compressed video communications today. These algorithms can perform dynamically in accordance with the varying channel conditions. The status of the channel is reported back to the video source by a number of receivers that have special traffic data compilation capabilities. These feedback reports make the video source more network-aware and thus contribute to efficiently adapting the flow control algorithms to the reported channel conditions at any instant of time.