A New Call Admission Control Mechanism for Multimedia Traffic over Next-Generation Wireless Cellular Networks

Abstract

Abstract-The subject of Call Admission Control (CAC) for wireless networks has been studied extensively in the literature. Another subject on which many researchers have focused their attention is that of video traffic modeling. However, user mobility, combined with the rapidly growing number of "greedy" multimedia applications, in terms of bandwidth and Quality of Service (QoS) requirements, form a challenging and yet unresolved problem for third and fourth-generation wireless networks. In recent work, we have built a Discrete Autoregressive (DAR (1)) model to capture the behavior of multiplexed H.263 videoconference movies from variable bit rate (VBR) coders. Based on this model, we propose in this work a new efficient CAC scheme for wireless cellular networks, which differs from the existing proposals in the literature in that it uses precomputed traffic scenarios combined with online simulation for its decision making. Our scheme is shown, via an extensive simulation study comparison and a conceptual comparison with well-known existing approaches, to clearly excel in terms of QoS provisioning to users receiving videoconference and Web traffic. To the best of our knowledge, this is the first work in the relevant literature where such an approach has been proposed.

Call Admission Control for wireless videoconference traffic based on the users' willingness to pay

“…This approach is especially plausible for wireless videoconference traffic, as the number of variations between source bandwidth requirements is naturally restricted by the type of application (a much larger pool of modes would have to be used in the case of regular video traffic). In [15] we have shown that our mechanism works equally well for H.263 videoconference traffic and excels, again, in comparison to the equivalent bandwidth approach.…”

“…The rationale behind this decision is that the arrival of a new user should cause the minimum possible number of degradations, and hence irritation, to users who are already in the system, therefore it is preferable that the new user is accepted with degradation. One point which needs to be stressed here is that in most of the relevant works in the literature (including our work in [2,15]), it is commonly accepted that handoff calls have absolute priority in obtaining an equal amount of channel bandwidth as the one they were occupying in their previous picocell location, i.e., handoff calls are not expected to endure any quality degradation, as this would lead to user dissatisfaction. We take a different approach in this work.…”

“…In recent work [2,15] we have proposed an efficient CAC mechanism for MPEG-4 and H.263 videoconference traffic, respectively. Our mechanisms adopted the idea of a probabilistic service, as described in [4].…”

“…Instead, we proposed the use of our modeling approach [16,17] for traffic originating from MPEG-4 and H.263 videoconference sources, in order to propose a new CAC mechanism for videoconference traffic transmission over wireless cellular networks. Our CAC mechanisms in [2,15] use the traffic parameters (peak, mean, standard deviation) which the video source either declares at call setup or has agreed on in its contract with the wireless provider, in order to precompute or compute online a large number of traffic scenarios for their decision-making.…”

“…Our mechanism makes decisions not only based on the system's ability to accommodate newly arriving users in terms of the estimated bandwidth they will need (as in our previous work in [2,15] and in the vast majority of the studies of the relevant literature), but also on the profit that can be made by the provider by choosing which users will be accepted and which will be degraded based on their willingness to pay for higher quality services, as stated in their contracts. When evaluating customer profitability, marketers are often reminded of the 80/20 rule (80% of the profits are produced by top 20% of profitable customers and 80% of the costs are produced by top 20% of unprofitable customers) [19].…”