A generic quantitative relationship between quality of experience and packet loss in video streaming services

2012 ◽  
Author(s):  
Sajad Khorsandroo ◽  
Rafidah Md Noor ◽  
Sayid Khorsandroo
Keyword(s):  
Video Streaming ◽  
Packet Loss ◽  
Quality Of Experience ◽  
Quantitative Relationship ◽  
Streaming Services

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.

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

Assessing quality of experience for high definition video streaming under diverse packet loss patterns

2013 ◽  
Vol 28 (8) ◽  
pp. 903-916 ◽  
Author(s):  
Mikołaj Leszczuk ◽  
Lucjan Janowski ◽  
Piotr Romaniak ◽  
Zdzisław Papir
Keyword(s):  
Video Streaming ◽  
Packet Loss ◽  
Quality Of Experience ◽  
High Definition ◽  
High Definition Video

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

2016 ◽  
Vol 102 ◽  
pp. 38-49 ◽  
Author(s):  
Qahhar Muhammad Qadir ◽  
Alexander A. Kist ◽  
Zhongwei Zhang
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Cross Layer ◽  
Streaming Services

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 ).

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