Fast Optimal Resource Allocation for Scalable Video Multicast in Broadband Wireless Networks

Li, P.; Zhang, H.; Zhao, Baohua; Rangarajan, Sampath

doi:10.1109/icc.2010.5502666

Cited by 7 publications

(1 citation statement)

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“…There is an increasing demand for high bandwidthconsuming services such as real-time video and video streaming over wireless access networks [1,2]. Video calls generally consume high bandwidth in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Multiple-RAT selection for reducing call blocking/dropping probability in cooperative heterogeneous wireless networks

Falowo

Chan

2012

J Wireless Com Network

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There is an increasing demand for high bandwidth-consuming services such as real-time video and video streaming over wireless access networks. A single radio access technology (RAT) in a heterogeneous wireless network may not always have enough radio resource to admit high bandwidth-consuming calls, such as video calls. Existing joint call admission control (JCAC) algorithms designed for heterogeneous wireless networks block/drop an incoming call when none of the available individual RATs in the network has enough bandwidth to admit the incoming call. Consequently, video calls experience high call blocking/dropping probability in the network. However, some calls such as multi-layer coded (scalable) video can be transmitted/ received over one or multiple RATs. This article proposes a JCAC algorithm that selects a single or multiple RATs for scalable video calls in heterogeneous wireless networks, depending on availability of radio resources in available RATs. Non scalable calls are always admitted into a single RAT by the algorithm. The aim of the proposed algorithm is to reduce call blocking/dropping probability for both scalable and non-scalable calls. An analytical model is developed for the proposed JCAC algorithm, and its performance is evaluated. Simulation results show that the proposed algorithm reduces call blocking/dropping probability in heterogeneous wireless networks.

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Section: Introductionmentioning

confidence: 99%