scholarly journals Crowdsourcing Framework for QoE-Aware SD-WAN

2021 ◽  
Vol 13 (8) ◽  
pp. 209
Author(s):  
Ibtihal Ellawindy ◽  
Shahram Shah Heydari
Keyword(s):  
Service Quality ◽  
Real Time ◽  
Quality Of Experience ◽  
Network Resources ◽  
Application Performance ◽  
Network Parameters ◽  
Subjective Assessments ◽  
Interactive Nature ◽  
Direct Feedback

Quality of experience (QoE) is an important measure of users’ satisfaction regarding their network-based services, and it is widely employed today to provide a real assessment of the service quality as perceived by the end users. QoE measures can be used to improve application performance, as well as to optimize network resources and reallocate them as needed when the service quality degrades. While quantitative QoE assessments based on network parameters may provide insights into users’ experience, subjective assessments through direct feedback from the users have also gathered interest recently due to their accuracy and interactive nature. In this paper, we propose a framework that can be used to collect real-time QoE feedback through crowdsourcing and forward it to network controllers to enhance streaming routes. We analyze how QoE can be affected by different network conditions, and how different streaming protocols compare against each other when the network parameters change dynamically. We also compare the real-time user feedback to predefined network changes to measure if participants will be able to identify all degradation events, as well as to examine which combination of degradation events are noticeable to the participants. Our aim is to demonstrate that real-time QoE feedback can enhance cloud-based services and can adjust service quality on the basis of real-time, active participants’ interactions.

scholarly journals State-of-the-Practice in Evaluation of Quality of Experience in Real-Life Environments

2013 ◽  
Vol 25 (3) ◽  
pp. 255-263 ◽  
Author(s):  
Marko Matulin ◽  
Štefica Mrvelj
Keyword(s):  
Service Quality ◽  
Quality Of Experience ◽  
Service Providers ◽  
Subjective Evaluation ◽  
Real Life ◽  
Network Resources ◽  
Switched Networks ◽  
Subjective Testing ◽  
Controlled Environments

Continuous monitoring of achieved level of service quality in packet-switched networks represents an activity of major importance for network and service providers. This is paramount for network resources provisioning which have to satisfy expectations of fickle customers. However, providing enough resources to specific user does not automatically increase their Quality of Experience (QoE), hence understanding of the relationship between these two is crucial in the network management process. Essentially, this requires subjective testing of service quality which is usually done in controlled environments such as laboratories. Nevertheless, the most accurate subjective evaluation of QoE includes real-life experiments in the environments where the services are actually used. Therefore, the aim of this paper is to provide a review of the current state-of-the-practice in evaluating QoE in real-life environments.

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

Quality of experience during psychosocial rehabilitation: A real-time investigation with experience sampling method.

2012 ◽  
Vol 35 (6) ◽  
pp. 447-453 ◽  
Author(s):  
Marta Bassi ◽  
Nicoletta Ferrario ◽  
Gabriella Ba ◽  
Antonella Delle Fave ◽  
Caterina Viganò
Keyword(s):  
Real Time ◽  
Quality Of Experience ◽  
Sampling Method ◽  
Psychosocial Rehabilitation ◽  
Experience Sampling ◽  
Experience Sampling Method

scholarly journals An Adaptive Bitrate Switching Algorithm for Speech Applications in Context of WebRTC

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
Mohannad Alahmadi ◽  
Peter Pocta ◽  
Hugh Melvin
Keyword(s):  
Real Time ◽  
Data Exchange ◽  
Quality Of Experience ◽  
End User ◽  
High Quality ◽  
Content Type ◽  
Switching Algorithm ◽  
Wide Range ◽  
The Impact

Web Real-Time Communication (WebRTC) combines a set of standards and technologies to enable high-quality audio, video, and auxiliary data exchange in web browsers and mobile applications. It enables peer-to-peer multimedia sessions over IP networks without the need for additional plugins. The Opus codec, which is deployed as the default audio codec for speech and music streaming in WebRTC, supports a wide range of bitrates. This range of bitrates covers narrowband, wideband, and super-wideband up to fullband bandwidths. Users of IP-based telephony always demand high-quality audio. In addition to users’ expectation, their emotional state, content type, and many other psychological factors; network quality of service; and distortions introduced at the end terminals could determine their quality of experience. To measure the quality experienced by the end user for voice transmission service, the E-model standardized in the ITU-T Rec. G.107 (a narrowband version), ITU-T Rec. G.107.1 (a wideband version), and the most recent ITU-T Rec. G.107.2 extension for the super-wideband E-model can be used. In this work, we present a quality of experience model built on the E-model to measure the impact of coding and packet loss to assess the quality perceived by the end user in WebRTC speech applications. Based on the computed Mean Opinion Score, a real-time adaptive codec parameter switching mechanism is used to switch to the most optimum codec bitrate under the present network conditions. We present the evaluation results to show the effectiveness of the proposed approach when compared with the default codec configuration in WebRTC.

scholarly journals Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking

2020 ◽  
Vol 10 (22) ◽  
pp. 8223
Author(s):  
Mykola Beshley ◽  
Peter Veselý ◽  
Andrii Pryslupskyi ◽  
Halyna Beshley ◽  
Marian Kyryk ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Quality Management ◽  
Service Quality ◽  
Quality Of Experience ◽  
Service Management ◽  
Customer Experience ◽  
Telecommunication Networks ◽  
Service Quality Management ◽  
Management Method

The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience and profitability, the service provider has been placing more and more attention on customer experience and QoE. The traditional quality of service management method based on SLA (Service Level Agreement) is not sufficient as a means to provide QoE-related contracts between service providers and customers. The current SLA method is mostly limited and focused on technical aspects of QoS (Quality of Service). Furthermore, they do not follow on the network the principles and semantic approach to the QoS specification for a communication service using QoE parameters. In this paper, we propose a customer-oriented quality of service management method for future IBN (Intent-Based Networking). It is based on a new QoE metric on a scale from 1 to 5, which allows one to take into account the commercial value of e-services for customers. Based on this approach, the network configuration and functionality of network equipment automatically changes depending on customer requirements. To implement the new method of service quality management, an algorithm for routing data packets in the network was developed, taking into account the current load of the forecast path. The algorithm of billing system functioning in conditions of customer-oriented quality management in telecommunication networks has been created. To investigate the effectiveness of the proposed method of service quality management with the traditional SLA method, we developed a simulation network model with the implementation of two approaches. By conducting a simulation, it was determined that the proposed method gives an average gain of 2–5 times for the criterion of the number of customers who require high quality of experience of the service.

Context-Aware Content Delivery

2015 ◽  
pp. 474-499
Author(s):  
Hassnaa Moustafa ◽  
V. Srinivasa Somayazulu ◽  
Yiting Liao
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Content Delivery ◽  
Context Aware ◽  
Video Delivery ◽  
Network Resources ◽  
Personalized Services ◽  
Over The Top ◽  
Required Quality

The huge changes in multimedia and video consumption styles are leading to different challenges for the current Internet architecture in order to support the required quality of experience. A comprehensive solution to these would help the service providers and over-the-top players (OTT) to differentiate their services and the network operators to handle ever growing demands on network resources in an era of slower growth in revenues. This chapter discusses the requirements for and approaches to enhanced content delivery architectures, video delivery standards and current and future content transport mechanisms. The chapter also discusses the Quality of Experience (QoE) metrics and management for video content and introduces context-awareness in the video delivery chain. It also provides several examples for context-aware content delivery and personalized services.

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