An evaluation of QoS for intensive video traffic over 802.11e WLANs

An evaluation of QoS for intensive video traffic over 802.11e WLANs

With the continuing development of the wireless technologies (Wi-Fi, 3G, 4G, WiMax and Bluethooth), the study of wireless multimedia transmissions has gained lately more attention. For example, the expectations of the company leaders on the growth of Wi-Fi video traffic has updated the lines of rese...

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Bibliographic Details
Published in:2015 International Conference on Electronics, Communications and Computers (CONIELECOMP) pp. 8 - 15
Main Authors: Perez, S., Facchini, H., Dantiacq, A., Cangemi, G., Campos, J.
Format: Conference Proceeding
Language:English
Published: IEEE 01-02-2015
Subjects:
Analytical models
EDCA parameters
IEEE 802.11e Standard
Measurement
modeling
Quality of service
simulation
Streaming media
throughput
tuning
WLAN 802.11e
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Summary:With the continuing development of the wireless technologies (Wi-Fi, 3G, 4G, WiMax and Bluethooth), the study of wireless multimedia transmissions has gained lately more attention. For example, the expectations of the company leaders on the growth of Wi-Fi video traffic has updated the lines of research on the standard IEEE 802.11e introduced to provide QoS (Quality of Service) to WLAN (Wireless LAN) networks. A quantitative analysis has been performed by simulation. We use a node model EDCA (Enhanced Distributed Channel Access) 802.11e with the tool Möbius of the University of Illinois, which supports an extension of SPN (Stochastic Petri Networks), known as HSAN (Hierarchical Stochastic Activity Networks). This formalism favors the comparison of the results with those obtained from other tools, based mainly on simulation languages, for Wi-Fi stations with the capacity to transmit voice, video or best effort traffic in presence of error. This article introduces novel scenario that varies the load by increasing the number of active stations from 5 to 45 but maintaining their relative traffic proportion. The proportion of traffic injected by stations is 65% video, 2% voice, and 33% best effort. Measured performance metrics were absolute or direct performance, relative performance, average delay of queue, and average queue size.
DOI:10.1109/CONIELECOMP.2015.7086933