scholarly journals Real-Time QoE Monitoring System for Video Streaming Services with Adaptive Media Playout

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Mingfu Li ◽  
Chien-Lin Yeh ◽  
Shao-Yu Lu
Keyword(s):  
Real Time ◽  
Video Streaming ◽  
Monitoring System ◽  
Quality Of Experience ◽  
Experimental Results ◽  
Media Player ◽  
Opinion Score ◽  
Streaming Services ◽  
The Mean

Quality of Experience (QoE) of video streaming services has been attracting more and more attention recently. Therefore, in this work we designed and implemented a real-time QoE monitoring system for streaming services with Adaptive Media Playout (AMP), which was implemented into the VideoLAN Client (VLC) media player to dynamically adjust the playout rate of videos according to the buffer fullness of the client buffer. The QoE monitoring system reports the QoE of streaming services in real time so that network/content providers can monitor the qualities of their services and resolve troubles immediately whenever their subscribers encounter them. Several experiments including wired and wireless streaming were conducted to show the effectiveness of the implemented AMP and QoE monitoring system. Experimental results demonstrate that AMP significantly improves the QoE of streaming services according to the Mean Opinion Score (MOS) estimated by our developed program. Additionally, some challenging issues in wireless streaming have been easily identified using the developed QoE monitoring system.

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

2021 ◽  
Vol 48 (4) ◽  
pp. 33-36
Author(s):  
Özge Celenk ◽  
Thomas Bauschert ◽  
Marcus Eckert
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Video Streaming ◽  
Quality Of Experience ◽  
Service Providers ◽  
Early Stage ◽  
Traffic Information ◽  
Streaming Traffic ◽  
Testing Accuracy

Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.

scholarly journals A quality of experience-aware cross-layer architecture for optimizing video streaming services

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Video Quality ◽  
The Internet ◽  
Cross Layer ◽  
Video Traffic ◽  
Adaptive Architecture ◽  
Streaming Services ◽  
Simulation Results

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

scholarly journals A Review of Predictive Quality of Experience Management in Video Streaming Services

2018 ◽  
Vol 64 (2) ◽  
pp. 432-445 ◽  
Author(s):  
Maria Torres Vega ◽  
Cristian Perra ◽  
Filip De Turck ◽  
Antonio Liotta
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Predictive Quality ◽  
Streaming Services

scholarly journals Enhancing Quality of Experience in Malay Language Dyslexia Screening Test

2021 ◽  
Vol 15 ◽  
pp. 99-103
Author(s):  
Muhammad Hanif Jofri ◽  
Mohd Norasri Ismail ◽  
Mohd Farhan Md Fudzee ◽  
Muhammad Hatta Mohamed Ali @ Md Hani
Keyword(s):  
Special Education Teachers ◽  
School System ◽  
Screening Test ◽  
Quality Of Experience ◽  
Screening Tests ◽  
Usual Practice ◽  
Mean Values ◽  
Opinion Score ◽  
The Mean

Identifying dyslexia among Malaysian citizens, especially children nowadays is a prominent issue. The usual practice of dyslexia screening tests in the Malaysian school system is by teacher's observation and intervention. However, this is usually time-consuming, less accurate and lacking additional supporting tools. Moreover, dyslexic children enrolling in a normal education system will encounter many problems for the teacher and the children themselves. A Malay language mobile-based application for a dyslexia screening test named Kiddo Disleksia has been developed to solve this issue. However, it has not been tested in terms of Quality of Experience (QoE) as well as usability. Therefore, this research aims to test the QoE level of Kiddo Disleksia and also to compare the traditional dyslexia screening test with Kiddo Disleksia in terms of usability. To test the QoE, several special education teachers are required to rate Kiddo Disleksia using Mean Opinion Score (MOS). Ten children were tested using Kiddo Disleksia and 80% of them recognized as dyslexic. This result also similar with the traditional paper-based screening test. Therefore, the Kiddo Disleksia application is considered reliable for dyslexia screening tests for children. For QoE, the results show that the mean values of MOS are 3.9 and above. Therefore, the quality of experience during dyslexia screening tests can be enhanced using Kiddo Disleksia.

scholarly journals User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1387
Author(s):  
Muhamad Hanif Jofri ◽  
Ida Aryanie Bahrudin ◽  
Noor Zuraidin Mohd Safar ◽  
Juliana Mohamed ◽  
Abdul Halim Omar
Keyword(s):  
Video Streaming ◽  
User Satisfaction ◽  
Quality Of Experience ◽  
Video Quality ◽  
Video Content ◽  
Selection Scheme ◽  
Content Selection ◽  
Opinion Score ◽  
Quality Video

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

Research on QoE Evaluation for Mobile Streaming Service over LTE Network

2014 ◽  
Vol 556-562 ◽  
pp. 5488-5492 ◽  
Author(s):  
Ya Jun Huang ◽  
We Nan Zhou ◽  
Li Yan You ◽  
Jian Chen ◽  
Yu Du
Keyword(s):  
Quality Of Experience ◽  
Exponential Model ◽  
Lte Network ◽  
Mobile Networking ◽  
Opinion Score ◽  
Network Transmission ◽  
The Mean ◽  
Streaming Service ◽  
Multiple Qos

The Quality of Experience (QoE) evaluation for mobile streaming service over LTE network is essential for mobile networking development. However, few QoE researches for mobile streaming service focus on LTE network. Based on the research of QoE for mobile streaming service over other networks, this paper achieves the simulation for network transmission for different videos using H.264 format on the ns-3 LTE simulation platform. What’s more, exponential model fittings are performed for not only single Quality of Service (QoS) parameter and QoE, but also multiple QoS parameters and QoE. Simulation results show that the network parameters and the Mean Opinion Score (MOS) of QoE have strong exponential relationships for streaming service over LTE network.

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