QoE-Aware Admission Control for Multimedia Applications in IEEE 802.11 Wireless Networks
Open accessAbstract
Widespread use of wireless networks nowadays raises many problems for service providers in managing their resources. These problems are caused mainly by restricted bandwidth and variable radio condition in this type of network. Moreover, with the emergence of multimedia traffic and its requirements in terms of quality, admission control is hence an inevitable choice to optimize network resources while maintaining high service quality at users. In this paper, we propose an admission control mechanism based on quality of experience (QoE) perceived by users. The human QoE is obtained by a tool called pseudo subjective quality assessment (PSQA), which is based on statistic learning using random neural network (RNN). Instead of relying on technical parameters such as bandwidth, loss, or latency, which do not correlate well with human perception, our scheme is based on mean opinion score (MOS) but without interaction from real humans. The simulation results demonstrate the better performance of our proposition compared to the loss-based approach regarding user satisfaction evaluated by achieved QoE at user and bandwidth utilization of the network evaluated by good put.
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Admission control has leveraged ML extensively in a variety of networks, including ATM networks, wireless networks, cellular networks, ad hoc networks, and next generation networks.
“As shown in Table 14, Admission control has leveraged ML extensively in a variety of networks, including ATM networks [95,189,190], wireless networks [8,36,359], cellular networks [66,67,281,372,458], ad hoc networks [452], and next generation networks [311].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [359]
Piamrat et al. propose an admission control mechanism for wireless networks based on subjective QoE perceived by end-users, in contrast to leveraging quantitative parameters such as bandwidth, loss, and latency.
“Piamrat et al. [359] propose an admission control mechanism for wireless networks based on subjective QoE perceived by end-users.This is in contrast to leveraging quantitative parameters, such as bandwidth, loss and latency.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [359]
No accuracy or error analysis is reported for the RandNN in Piamrat et al.
“Though, they evaluate their admission control mechanism for user satisfaction and throughput based metrics, no accuracy or error analysis is reported for the RandNN.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [359]
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- Authors
- Piamrat, K., Ksentini, A., Viho, C., & Bonnin, J.-M.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Institut de Recherche en Informatique et Systèmes Aléatoires (FR)
- Télécom Bretagne (FR)
- Venue
- 2008 IEEE 68th Vehicular Technology Conference
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 1-5
- ISSN
- 1090-3038 (ISSN-L)
- OpenAlex
- W2159468364
- Cited by
- 59
- References
- 6
- Citations by year
- 2023: 4 · 2022: 2 · 2021: 1 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 6 · 2015: 5 · 2014: 7 · 2013: 4 · 2012: 7
- FWCI
- 3.86
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- other-oa
- Locations
- IEEE Vehicular Technology Conference (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- quality of experience, admission control, multimedia traffic, IEEE 802.11, wireless networks, random neural networks, mean opinion score, bandwidth utilization, user satisfaction
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