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In this paper, we investigate the problem of multiuser resource management in multihop cognitive radio networks for delay-sensitive applications. Since tolerable delay does not allow propagating global information back and forth throughout the multihop network to a centralized decision maker, the source nodes and relays need to adapt their actions (transmission frequency channel and route selections) in a distributed manner, based on local network information. We propose a distributed resource-management algorithm that allows network nodes to exchange information and that explicitly considers the delays and cost of exchanging the network information over multihop cognitive radio networks. In this paper, the term ldquocognitiverdquo refers to both the capability of the network nodes to achieve large spectral efficiencies by dynamically exploiting available frequency channels and their ability to learn the ldquoenvironmentrdquo (the actions of interfering nodes) based on the designed information exchange. Note that the node competition is due to the mutual interference of neighboring nodes using the same frequency channel. Based on this, we adopt a multiagent-learning approach, i.e., adaptive fictitious play, which uses the available interference information. We also discuss the tradeoff between the cost of the required information exchange and the learning efficiency. The results show that our distributed resource-management approach improves the peak signal-to-noise ratio (PSNR) of multiple video streams by more than 3 dB, as opposed to the state-of-the-art dynamic frequency channel/route selection approaches without learning capability, when the network resources are limited.