Video QoE killer and performance statistics in WebRTC-based video communication

Ammar, Doreid; Moor, Katrien De; Xie, Min; Fiedler, Markus; Heegaard, Poul E.

doi:10.1109/cce.2016.7562675

Cited by 37 publications

(9 citation statements)

References 14 publications

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“…Data from two-and three-party video calls was collected under different CPU limitations and network impairments, using the WebRTC testbed described in [12]. Previous work [2], [3] has provided insights into the usefulness (and limitations) of WebRTC performance statistics gathered by Google Chrome and in the root causes of technical impairments that are perceivable (e.g., visually and/or auditory) by users. The work presented in this paper goes a step further and applies ML algorithms to the gathered datasets in order to estimate video blockiness, audio distortions, and to identify root causes of problems.…”

Section: A Overviewmentioning

confidence: 99%

“…The statistics are collected per browser. Despite all the challenges, previous work has demonstrated the potential of using these statistics to study QoE aspects of WebRTC services [2], [3].…”

Section: B Data Collectionmentioning

confidence: 99%

“…In this paper, we focus on exploring the usefulness of machine learning (ML) in the context of WebRTC performance estimation and root cause analysis from a service provider perspective. Previous work [2], [3] has investigated the usefulness and limitations of the WebRTC performance statistics gathered by Google Chrome for QoE studies. We now go one step further and aim to predict the occurrence of perceivable quality impairments for the user and to identify root causes underlying these impairments by using WebRTC performance statistics as input features for ML-based classifiers.…”

Section: Introductionmentioning

confidence: 99%

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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)

Self Cite

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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.

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Section: A Overviewmentioning

confidence: 99%

Section: B Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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)

Self Cite

View full text Add to dashboard Cite

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“…Our performance evaluation focuses on a few key metrics such as data rate, frame rate, Round Trip Time (RTT), and call setup time, which have been shown to be the key factors that affect the user video experience [20,3]. Overall, this paper presents a thorough performance evaluation of WebRTC and discusses various performance-related tradeoffs.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of WebRTC-based Video Conferencing

Jansen

Goodwin

Gupta

et al. 2018

SIGMETRICS Perform. Eval. Rev.

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WebRTC has quickly become popular as a video conferencing platform, partly due to the fact that many browsers support it. WebRTC utilizes the Google Congestion Control (GCC) algorithm to provide congestion control for realtime communications over UDP. The performance during a WebRTC call may be influenced by several factors, including the underlying WebRTC implementation, the device and network characteristics, and the network topology. In this paper, we perform a thorough performance evaluation of WebRTC both in emulated synthetic network conditions as well as in real wired and wireless networks. Our evaluation shows that WebRTC streams have a slightly higher priority than TCP flows when competing with cross traffic. In general, while in several of the considered scenarios WebRTC performed as expected, we observed important cases where there is room for improvement. These include the wireless domain and the newly added support for the video codecs VP9 and H.264 that does not perform as expected.

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“…Methodology: With our third study, we shifted from using the Samsung browser to using Chrome, as this provided the opportunity to access the webrtc-internals tool implemented within Chrome [6]. Webrtc-internals is an internal functionality for collecting statistics about ongoing WebRTC sessions [44]. To obtain statistics, a session has to be opened in the Chrome browser, and while in that session, another tab has to be open with the following URL: chrome://webrtcinternals.…”

Section: ) User Studymentioning

confidence: 99%

QoE Assessment of Mobile Multiparty Audiovisual Telemeetings

Vucic

Skorin‐Kapov

2020

IEEE Access

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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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Video QoE killer and performance statistics in WebRTC-based video communication

Cited by 37 publications

References 14 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

Performance Evaluation of WebRTC-based Video Conferencing

QoE Assessment of Mobile Multiparty Audiovisual Telemeetings

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