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

Ammar, Doreid; Moor, Katrien De; Skorin‐Kapov, Lea; Fiedler, Markus; Heegaard, Poul E.

doi:10.1109/lcn44214.2019.8990677

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…With this work, we have extended our understanding of how different quality degradations during telemeetings are related and how they affect users. The paper complements other research (e.g., the discoveries presented by Ammar et al in [14]), which focused on predicting different audiovisual disturbances from network data flows. Our findings show how those disturbances may affect users.…”

Section: Discussionsupporting

confidence: 72%

“…An example of using network-related parameters to predict the appearance of audiovisual disturbances during multiparty telemeetings can be found in [14], where a machine learning technique was used for WebRTC performance estimation. In a series of papers published after four years of research, Vučić et al analyzed the impact of different smartphone configurations [15], video resolutions and bandwidth [16], and packet loss [17] on user QoE for telemeetings in the mobile environment, while investigating the unexpected quality disturbances during WebRTC sessions in [18].…”

Section: Literature Review and Motivationmentioning

confidence: 99%

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Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Mrvelj

Matulin

2021

Electronics

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Modern users are accustomed to always-accessible networks ready to serve all of their communication, entertainment, information, and other needs, at the touch of their devices. Spoiled with choices provided on the competitive markets, the risk of customer churn makes network and service providers sensitive to user Quality of Experience (QoE). Services that enable people to work and industries to function in these pandemic times, such as the telemeeting service, are becoming ever more critical, not just for the end-users but also for the providers. Nevertheless, the heterogeneity of end-users network environments and the uniqueness of the service (bidirectional video and audio transmissions and interactivity between the meeting peers) imposes specific QoE requirements. Hence, this paper focuses on understanding how different service quality degradations affect user perception and frustration with such impaired service. The impact of eight quality degradations was analyzed. Based on the conducted user study, we used the multiple regression analysis and developed three models capable of predicting user Level of Frustration (LoF) for the specific degradations that we have analyzed. The models work with the User Frustration Susceptibility Index (UFSI), which categorizes users into groups based on their tendency to become frustrated with the impaired service.

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Section: Discussionsupporting

confidence: 72%

Section: Literature Review and Motivationmentioning

confidence: 99%

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Mrvelj

Matulin

2021

Electronics

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“…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: 60%

“…They collected WebRTC-internal statistics from web browsers and attempted to categorize video blockiness and audio distortion into acceptable and unacceptable classes. They leveraged C4.5, random forest, naïve Bayes, SMO, IBK, and bagging in WEKA and achieved 92% and 82% accuracy in categorizing video blockiness and audio distortion into binary classes, respectively [20]. On the other hand, Hora et al investigated the perceived quality of RTC video over Wi-Fi networks by measuring the SSIM of video and the PESQ of audio communications.…”

Section: Related Workmentioning

confidence: 99%

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

Morshedi

Noll

2021

Future Internet

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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.

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“…The possibility to estimate perceivable quality impairments in terms of blockiness and audio distortion using machine learning, and to predict the occurrence of disturbances was investigated in [34]. The authors studied call scenarios with no impairments and with realistic technical impairments (packet loss and delays).…”

Section: B Related Studies On Qoe For Multiparty Audiovisual Telemeementioning

confidence: 99%

QoE Assessment of Mobile Multiparty Audiovisual Telemeetings

Vucic

Skorin‐Kapov

2020

IEEE Access

Self Cite

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The expectations of modern mobile users are increasingly moving towards being able to access demanding services regardless of context or system influence factors, such as network conditions, service topology, and device processing capabilities. Multiparty audiovisual telemeetings are an example of a real-time, delay sensitive, and heavy load service, demanding to run on smartphones that are limited in display size, processing power, and battery capacity. In this paper, we first provide an overview of multiparty audiovisual calls established via mobile devices and key aspects influencing Quality of Experience (QoE). We then report on the results of five user studies conducted over the course of the past 4 years, focused on investigating the impact of video quality in terms of different video encoding parameter configurations (namely bitrate, frame rate, and resolution) on subjective QoE scores for WebRTC-based video calls. We identify lower and upper bounds on video configuration parameters when used in the context of three-party calls. Results have shown that in certain cases it is better to provide constant lower objective video quality than to switch between higher and lower qualities, since participants start to perceive impairments. Finally, we investigate the relationship between objectively measured video quality impairments (blurriness and blockiness) and subjective user scores. Obtained results indicate that the Birnbaum-Saunders distribution for blockiness and the Burr and Gamma distributions for blurriness provide good fits for quality ratings. Gathered results aim to provide input for deriving QoE-aware service adaptation strategies, enabling increased resource allocation efficiency while maintaining acceptable end-user QoE.

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

Cited by 12 publications

References 15 publications

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

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

QoE Assessment of Mobile Multiparty Audiovisual Telemeetings

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