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 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 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 Novel Traffic Rate Measurement Algorithm for Quality of Experience-Aware Video Admission Control

2020 ◽  
Author(s):  
Alexander A. Kist ◽  
qahhar muhammad qadir ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Video Quality ◽  
The Internet ◽  
Video Traffic ◽  
Internet Service ◽  
Trade Off ◽  
Available Bandwidth ◽  
Traffic Rate

With the inevitable dominance of video traffic on the Internet, providing perceptually good video quality is becoming a challenging task. This is partly due to the bursty nature of video traffic, changing network conditions and limitations of network transport protocols. This growth of video traffic has made Quality of Experience (QoE) of the end user the focus of the research community. In contrast, Internet service providers are concerned about maximizing revenue by accepting as many sessions as possible, as long as customers remain satisfied. However, there is still no entirely satisfactory admission algorithm for flows with variable rate. The trade-off between the number of sessions and perceived QoE can be optimized by exploiting the bursty nature of video traffic. This paper proposes a novel algorithm to determine the upper limit of the aggregate video rate that can exceed the available bandwidth without degrading the QoE of accepted video sessions. A parameter $\beta$ that defines the exceedable limit is defined. The proposed algorithm results in accepting more sessions without compromising the QoE of on-going video sessions. Thus it contributes to the optimization of the QoE-Session trade-off in support of the expected growth of video traffic on the Internet.

scholarly journals QoE-Aware Cross-Layer Architecture for Video Traffic over Internet

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Rate Adaptation ◽  
Cross Layer ◽  
Foreseeable Future ◽  
Application Layer ◽  
Video Traffic ◽  
Transmission Rates ◽  
Active Video ◽  
Simulation Results

The emergence of video applications and video capable devices have contributed substantially to the increase of video traffic on Internet. New mechanisms recommending video rate adaptation towards delivering enhanced Quality of Experience (QoE) at the same time making room for more sessions. This paper introduces a cross-layer QoE-aware architecture for video traffic over the Internet. It proposes that video sources at the application layer adapt their rate to the network environment by controlling their transmitted bit rate dynamically; and the edge of network at the network layer protects the quality of the active video sessions by controlling the acceptance of new session through a video-aware admission control. In particular, it will seek the most efficient way of accepting new video session and adapting transmission rates to free up resources for more session while maintaining the QoE of active sessions. The proposed framework will contribute to the preparation for the extreme growth of video traffic in the foreseeable future. Simulation results show that the proposed cross-layer architecture guarantees the QoE for the admitted sessions and utilizes the link more efficiently comparing to the rate adaptation only architecture.

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 Novel Traffic Rate Measurement Algorithm for Quality of Experience-Aware Video Admission Control

2020 ◽  
Author(s):  
Alexander A. Kist ◽  
qahhar muhammad qadir ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Video Quality ◽  
The Internet ◽  
Video Traffic ◽  
Internet Service ◽  
Trade Off ◽  
Available Bandwidth ◽  
Traffic Rate

With the inevitable dominance of video traffic on the Internet, providing perceptually good video quality is becoming a challenging task. This is partly due to the bursty nature of video traffic, changing network conditions and limitations of network transport protocols. This growth of video traffic has made Quality of Experience (QoE) of the end user the focus of the research community. In contrast, Internet service providers are concerned about maximizing revenue by accepting as many sessions as possible, as long as customers remain satisfied. However, there is still no entirely satisfactory admission algorithm for flows with variable rate. The trade-off between the number of sessions and perceived QoE can be optimized by exploiting the bursty nature of video traffic. This paper proposes a novel algorithm to determine the upper limit of the aggregate video rate that can exceed the available bandwidth without degrading the QoE of accepted video sessions. A parameter $\beta$ that defines the exceedable limit is defined. The proposed algorithm results in accepting more sessions without compromising the QoE of on-going video sessions. Thus it contributes to the optimization of the QoE-Session trade-off in support of the expected growth of video traffic on the Internet.

scholarly journals QoE-Aware Cross-Layer Architecture for Video Traffic over Internet

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Rate Adaptation ◽  
Cross Layer ◽  
Foreseeable Future ◽  
Application Layer ◽  
Video Traffic ◽  
Transmission Rates ◽  
Active Video ◽  
Simulation Results

The emergence of video applications and video capable devices have contributed substantially to the increase of video traffic on Internet. New mechanisms recommending video rate adaptation towards delivering enhanced Quality of Experience (QoE) at the same time making room for more sessions. This paper introduces a cross-layer QoE-aware architecture for video traffic over the Internet. It proposes that video sources at the application layer adapt their rate to the network environment by controlling their transmitted bit rate dynamically; and the edge of network at the network layer protects the quality of the active video sessions by controlling the acceptance of new session through a video-aware admission control. In particular, it will seek the most efficient way of accepting new video session and adapting transmission rates to free up resources for more session while maintaining the QoE of active sessions. The proposed framework will contribute to the preparation for the extreme growth of video traffic in the foreseeable future. Simulation results show that the proposed cross-layer architecture guarantees the QoE for the admitted sessions and utilizes the link more efficiently comparing to the rate adaptation only architecture.

scholarly journals A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hongyun Zheng ◽  
Yongxiang Zhao ◽  
Xi Lu ◽  
Rongzhen Cao
Keyword(s):  
Quality Of Experience ◽  
Fog Computing ◽  
Video Quality ◽  
Adaptive Streaming ◽  
Network Operator ◽  
Video Traffic ◽  
Client Server ◽  
Prediction Scheme ◽  
Network Equipment

Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.

scholarly journals Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

2020 ◽  
Vol 10 (5) ◽  
pp. 1793
Author(s):  
Lina Du ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Jing Zhang ◽  
Xiaoguang Li ◽  
...  
Keyword(s):  
Video Compression ◽  
Quality Of Experience ◽  
Feature Vector ◽  
Video Quality ◽  
Superior Performance ◽  
Adaptive Streaming ◽  
Input Feature ◽  
Streaming Services ◽  
Content Characteristics

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.

scholarly journals Mechanisms for QoE optimisation of Video Traffic: A review paper

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Review Paper ◽  
End Users ◽  
The Internet ◽  
Video Traffic ◽  
Future Directions ◽  
The Past ◽  
New Methods ◽  
Increasing Demand

Transmission of video traffic over the Internet has grown exponentially in the past few years<br>with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users’ video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

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