Experimental QoE Evaluation of Multicast Video Delivery over IEEE 802.11aa WLANs

Gringoli, Francesco; Serrano, Pablo; Ucar, Iñaki; Facchi, Nicolò; Azcorra, Arturo

doi:10.1109/tmc.2018.2876000

Cited by 17 publications

(3 citation statements)

References 29 publications

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“…The authors in [17] provide an experimental evaluation that provides insights into the trade-offs of each policy. Another experimental evaluation focused on QoE is presented in [18]. All these evaluations seem to converge on one conclusion: there is not a single GATS policy that is good for everything, and its selection should be based on network conditions.…”

Section: Related Workmentioning

confidence: 96%

Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI

Gómez

Coronado

Villalón

et al. 2022

Wireless Communications and Mobile Computing

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COVID-19 has changed the way we use networks, as multimedia content now represents an even more significant portion of the traffic due to the rise in remote education and telecommuting. In this context, in which Wi-Fi is the predominant radio access technology (RAT), multicast transmissions have become a way to reduce overhead in the network when many users access the same content. However, Wi-Fi lacks a versatile multicast transmission method for ensuring efficiency, scalability, and reliability. Although the IEEE 802.11aa amendment defines different multicast operation modes, these perform well only in particular situations and do not adapt to different channel conditions. Moreover, methods for dynamically adapting them to the situation do not exist. In view of these shortcomings, artificial intelligence (AI) and machine learning (ML) have emerged as solutions to automating network management. However, the most accurate models usually operate as black boxes, triggering mistrust among human experts. Accordingly, research efforts have moved towards using Interpretable-AI models that humans can easily track. Thus, this work presents an Interpretable-AI solution designed to dynamically select the best multicast operation mode to improve the scalability and efficiency of this kind of transmission. The evaluation shows that our approach outperforms the standard by up to 38%.

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Section: Related Workmentioning

confidence: 96%

Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI

Gómez

Coronado

Villalón

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…To outperform IEEE 802.11e operation for real-time multimedia content delivery, IEEE 802.11aa introduced the intra-AC traffic differentiation functionality, with the definition of two new time-critical voice and video ACs [ 11 ]. The capabilities of IEEE 802.11e/aa enhancements to improve the performance of delay-sensitive traffic in WLANs has been widely considered in the literature [ 12 , 13 , 14 ]. However, in general, none of the IEEE 802.11 mechanisms guarantee the quality of service of heterogeneous real-time streams when a WLAN is overloaded [ 15 ].…”

Section: Limitations Of Ieee 80211 To Handle Time-sensitive Trafficmentioning

confidence: 99%

Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7

Adame

Carrascosa

Bellalta

2021

Sensors

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A short time after the official launch of WiFi 6, IEEE 802.11 working groups along with the WiFi Alliance are already designing its successor in the wireless local area network (WLAN) ecosystem: WiFi 7. With the IEEE 802.11be amendment as one of its main constituent parts, future WiFi 7 aims to include time-sensitive networking (TSN) capabilities to support low latency and ultra-reliability in license-exempt spectrum bands, enabling many new Internet of Things scenarios. This article first introduces the key features of IEEE 802.11be, which are then used as the basis to discuss how TSN functionalities could be implemented in WiFi 7. Finally, the benefits and requirements of the most representative Internet of Things low-latency use cases for WiFi 7 are reviewed: multimedia, healthcare, industrial, and transport.

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“…On the other hand, although many services generate WLAN traffic, two widely and major used WLAN traffics, audio service and video service, are more than 60% traffic over total IEEE 802.11 WLAN traffic [21]. Therefore, investigating the statistics properties of busy/idle duration of audio and video services and their predictability can benefit the CR technique used for WLAN system [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

WLAN Channel Status Duration Prediction for Audio and Video Services Using Probabilistic Neural Networks

Hou,

Denno

2024

IEEE Access

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Due to massive increase in wireless access from smartphones, IoT devices, WLAN is aiming to improve its spectrum efficiency (SE) using many technologies. Some interesting techniques for WLAN systems are flexible allocation of frequency resource and cognitive radio (CR) techniques which expect to find more useful spectrum resource by modeling and then predicting of channel status using the captured statistics information of the used spectrum. This paper investigates the prediction accuracy of busy/idle duration of two major wireless services: audio service and video service using neural network based predictor. We first study the statistics distribution of their time-series busy/idle (B/I) duration, and then analyze the predictability of the busy/idle duration based on the predictability theory. Then, we propose a data categorization (DC) method which categorizes the duration of recent B/I duration according the their ranges to make the duration of next data be distributed into several streams. From the predictability analysis of each stream and the prediction performance using the probabilistic neural network (PNN), it can be confirmed that the proposed DC can improve the prediction accuracy of time-series data in partial streams.INDEX TERMS Channel status duration prediction; WLAN audio/video traffic; Data predictability analysis; probabilistic neural network (PNN).

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Experimental QoE Evaluation of Multicast Video Delivery over IEEE 802.11aa WLANs

Cited by 17 publications

References 29 publications

Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI

Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI

Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7

WLAN Channel Status Duration Prediction for Audio and Video Services Using Probabilistic Neural Networks

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