Performance Evaluation of WebRTC-based Video Conferencing

Jansen, Bart M. P.; Goodwin, Timothy J. D.; Gupta, Varun; Kuipers, Fernando A.; Zussman, Gil

doi:10.1145/3199524.3199534

Cited by 80 publications

(34 citation statements)

References 24 publications

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“…Moreover, the transmission rate of WebRTC is able to adapt to the available bandwidth of the network, whereas Skype platform adjusts the video resolution to adapt to the network condition [20,21,24]. Similar findings were observed by another study reported in [25], which indicated that the WebRTC-based video conference system could perform well on the network less than 1 Mbps of bandwidth capacity. These outcomes supported the results of this study.…”

Section: Discussionsupporting

confidence: 66%

A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

Chai

Nguyen

2020

Applied Sciences

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This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications.

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Section: Discussionsupporting

confidence: 66%

A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

Chai

Nguyen

2020

Applied Sciences

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“…Focusing on the impact of network performance, Jansen et al [6] investigate the effects of latency, packet loss, and bandwidth on the performance of WebRTC-based videoconferencing in both synthetic and real wired and wireless environments. In particular, they focus on the impact of the Google Congestion Control (GCC) algorithm, utilized by WebRTC to provide congestion control for real-time communications over UDP.…”

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)

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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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“…Filtering noise without altering the speech quality is a huge challenge. It can be done by using software filters (e.g., WebRTC library [15][16][17]) and/or hardware filters [18]. However, incidental noise may require a sophisticated active noise reduction.…”

Section: Influence Of Dynamically Changing Environmentmentioning

confidence: 99%

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

Podpora

Gardecki

Beniak

et al. 2020

Sensors

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This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot's interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems-Bluetooth Human Identification Smart Subsystem-was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area.The days, when simply having a robot was impressive, are gone. The technology should be more than just presenting content. It should be truly interactive. The authors of this work evaluated methods for the purpose of measurement and influencing the Human-Robot interaction User Experience, (see Reference [1]), concluding that the robot's interaction is perceived as more natural if the robot is able to personalize its communication content towards a particular interlocutor. It became clear, that the system should be able to get as much information as possible during and before the conversation takes place [2]. One of the key aspects in a healthy conversation is emotional state recognition. All the improvements of the robots presented in this paper are aiming for natural, real integration with human customers with the implementation of Artificial Intelligence [3,4].The authors of this work have indicated extending the robot's capabilities by adding additional external wireless sensors in Reference [2], however without Smart Infrastructure. Implementation of a remote PID (Passive Infrared Detector) sensor in order to get a signal "someone is coming" seemed to be enough at that stage.In this paper an extended approach is being presented, introducing Human Identification Smart Subsystems (HISS)-a set of external sensors, which enable us to obtain much more valuable information, resulting from the communication with the Smart Infrastructure [5] or databases of the customer. The idea presented in this paper has been successfully verified and implemented and the con...

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Performance Evaluation of WebRTC-based Video Conferencing

Cited by 80 publications

References 24 publications

A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

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

Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors

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