Predicting Freezing of WebRTC Videos in WiFi Networks

Yan, Sheng; Guo, Yuchun; Chen, Yishuai; Xie, Feng

doi:10.1007/978-3-030-05888-3_27

Cited by 6 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further looking to relate measurable QoS metrics to QoE, Yan et al [11] conduct measurements in a WiFi network and build an ML model to infer whether QoE is acceptable or not in the next time window based on current QoS metrics (including Round-Trip Time, Link Quality, and Received Signal Strength Indicator (RSSI)). As a proxy measure for QoE, they rely on detecting video freezing events.…”

Section: Background and Related Workmentioning

confidence: 99%

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Ammar

Moor

Skorin‐Kapov

et al. 2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

View full text Add to dashboard Cite

We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Ammar

Moor

Skorin‐Kapov

et al. 2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

View full text Add to dashboard Cite

show abstract

“…The results demonstrate that our approach achieved higher classification accuracy than the results presented in other studies [3,[17][18][19][20], even though related works have often used binary classification, which is essentially a simpler classification problem than multi-class prediction. Existing approaches have also used external measurements to collect generic network performance parameters for training ML algorithms and identifying quality degradation.…”

Section: Discussionmentioning

confidence: 61%

“…They further categorized experiments as unacceptable when a session experienced bad QoE for more than 30% of the session. Using the decision tree, random forest, SVM, and extra tree classifiers with an 80-20% training-testing split, they achieved the highest F1 score, 0.74, with the decision tree [17,18]. Sulema et al investigated the QoE of WebRTC in mobile broadband networks using the MONROE platform.…”

Section: Related Workmentioning

confidence: 99%

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

Morshedi

Noll

2021

Future Internet

View full text Add to dashboard Cite

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

show abstract

“…In a related effort, the authors of [188] conducted evaluations using WebRTC measurements from a WiFi network. Using QoD metrics such as WiFi RTT, Link Quality, and RSSI, the authors employed ML models to predict whether the video quality would be acceptable during the next time window.…”

Section: Predicting the Video Quality In Wireless Settingsmentioning

confidence: 99%

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

2021

View full text Add to dashboard Cite

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

show abstract

Predicting Freezing of WebRTC Videos in WiFi Networks

Cited by 6 publications

References 9 publications

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

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

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

Contact Info

Product

Resources

About