Quality of Experience Assessment of Video Streaming

This study aims to determine the user’s satisfaction level of online streaming by using different web browsers. At the client layer, the assessment of the user’s QoE is conducted by evaluating the performance of three web browsers (Google Chrome, Mozilla Firefox, and Internet Explorer). We took the subjective test by conducting different experiments with the users and ask the users to assign ratings on the provided questionnaires, and from those ratings, we calculated results in the form of Mean Opinion Score.

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Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

Applied Sciences ◽

10.3390/app9112297 ◽

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.

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

Baghdad Science Journal ◽

10.21123/bsj.2021.18.4(suppl.).1387 ◽

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.

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

International Journal of Digital Multimedia Broadcasting ◽

10.1155/2018/2619438 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 7

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.

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