Proactive multicasting with predictable demands

In a recent work, we have introduced the notion of proactive resource allocation in wireless networks whereby the predictability of user demands are leveraged to significantly enhance the spectral efficiency of the network in outage limited regimes. In this paper, we expand the horizon to the important scenario of multicast traffic. Our analysis reveals two additional types of gains that can be leveraged in this proactive multicast scenario. The first can be attributed to the basic nature of multicast traffic in which each request would represent a data source rather than a user, as it would in the unicast case. The second is the demand alignment phenomenon whereby the predictive network would wait to gather as much requests as possible and serve them altogether using the same resources. We analytically derive the impact of these advantages on the system diversity gain, which quantifies the exponential decay rate of the outage probability, and further illustrate the resulting gains via numerical results. I. INTRODUCTION In (1), (2), we have introduced a novel scheme for resource allocation that exploits the predictability of the wireless net- work traffic to significantly reduce the capacity required to maintain a target level of Quality-of-Service (QoS) defined as the outage probability or improve the QoS performance given a certain capacity limit. We have addressed scenarios when the network is unicast and characterized the asymptotic decay rate of the outage probability with the network capacity which we have defined as the diversity gain. We have shown that the new proactive resource allocation technique improves the diversity gain of the network by a factor of T + 1, where T is a prediction interval by which the wireless network can anticipate users' requests in advance