Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Morshedi, Maghsoud; Noll, Josef

doi:10.3390/s21020621

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Numerous approaches have been suggested to address the demands of secure video communication over noisy channels. These solutions encompass techniques such as Adaptive Modulation (AM), Forward Error Correction (FEC), Automatic Repeat Request (ARQ), Joint Source-Channel Coding (JSCC) for retransmission, robust source coding, adaptive channelsource coding, data partitioning, post-processing error concealment, and scalable transportpriority coding [12][13][14][15].…”

Section: Technical Characteristics Of Video Transmission In Non-wired...mentioning

confidence: 99%

Optimization of Video Transmission Quality in Wireless Networks

Țurcanu,

Țurcanu

2024

J. Eng. Sci.

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The transmission of video stream over wireless networks is used by many applications and services, from the use of equipment in home communication networks to those carrying the video surveillance stream. Numerous technical challenges may arise when the unpredictable characteristics of the radio channel align or do not align with the stipulated requirements for transmission bandwidth and the necessary latency for transporting video data. All these challenges prevent users from experiencing smooth video streaming. Depending on the application scenarios, video service emphasis may vary according of Quality of Experience - QoE or parameter used traditionally in the networks, Quality of Service - QoS. The purpose of the research consists in optimization of the flow service, which ensures the adaptability of video communication to changes of conditions over the wireless computer network. As a result of a thorough analysis of the video playback characteristics related to the wireless channel, the most optimal approach in optimizing the quality of the video stream service over a wireless LAN requiring minimal modifications was identified, and namely system centric approach.

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Section: Technical Characteristics Of Video Transmission In Non-wired...mentioning

confidence: 99%

Optimization of Video Transmission Quality in Wireless Networks

Țurcanu,

Țurcanu

2024

J. Eng. Sci.

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“…Morshedi and Noll propose a novel ML-based approach for estimating the perceived QoS of video streaming [209] and video conferencing [210] using only 801.11-specific network performance parameters collected from AP. The studies use datasets comprising 802.11n/ac/ax specific network performance parameters in the form of mean opinion scores.…”

Section: Predicting the Health Of Wi-fi Connectionsmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

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“…Morshedi and Noll [182] propose a novel ML-based approach for estimating the perceived QoS of video streaming using only 802.11-specific network performance parameters collected from AP. The study produced datasets comprising 802.11n/ac/ax specific network performance parameters in the form of mean opinion scores.…”

Section: Predicting the Health Of Wifi Connectionsmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

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Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Cited by 7 publications

References 15 publications

Optimization of Video Transmission Quality in Wireless Networks

Optimization of Video Transmission Quality in Wireless Networks

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

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