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 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

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 ).

Quality of Experience-based Routing of Video Traffic for Overlay and ISP Networks

2018 ◽  
Author(s):  
Giacomo Calvigioni ◽  
Ramon Aparicio-Pardo ◽  
Lucile Sassatelli ◽  
Jeremie Leguay ◽  
Paolo Medagliani ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Video Traffic

scholarly journals Obtaining More Realistic Cross-Layer QoS Measurements: A VoIP over LTE Use Case

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
F. Javier Rivas ◽  
Almudena Díaz ◽  
Pedro Merino
Keyword(s):  
Quality Of Experience ◽  
Access Network ◽  
Added Value ◽  
Cross Layer ◽  
Use Case ◽  
Lte Networks ◽  
Testing Environment ◽  
Radio Access ◽  
Open Issue

We introduce a real-time experimentation testbed in this paper which enables more realistic analysis of quality of service (QoS) in LTE networks. This testbed is envisioned for the improvement of QoS and quality of experience (QoE) through the experimentation with real devices, services, and radio configurations. Radio configurations suggested in the literature typically arise from simulations; the testbed provides a real and controlled testing environment where such configurations can be validated. The added value of this testbed goes a long way not only in the provision of more realistic results but also in the provision of QoS and QoE cross-layer measurements through the correlation of information collected at different layers: from service and IP levels to radio and protocol parameters. Analyzing the interlayer dependencies will allow us to identify optimal settings for the radio access network and service parameters. This information can be used to suggest new cross-layer optimizations to further improve quality of experience of mobile subscribers. As a use case, we examine VoIP service over LTE, which is currently an open issue.

scholarly journals Cross-Layer Optimization for Efficient Delivery of Scalable Video over WiMAX

2016 ◽  
Vol 855 ◽  
pp. 171-177 ◽  
Author(s):  
Lung Jen Wang ◽  
Chiung Yun Chang ◽  
Jen Yi Huang
Keyword(s):  
Scalable Video ◽  
Cross Layer ◽  
Video Delivery ◽  
Application Layer ◽  
Mac Layer ◽  
Video Streams ◽  
Delivery Performance ◽  
Cross Layer Optimization ◽  
Efficient Delivery

Video delivery application over wireless network has become increasingly important in recent years. For achieving more flexible video streams transmission, the scalable extension of the H.264/AVC standard has been developed, as well as which is combined with temporal, spatial and quality scalabilities to adapt efficiently the wireless bandwidth resource. In this paper, a cross-layer optimization for robust H.264 scalable video delivery over WiMAX network is proposed. In order to obtain a better delivery performance, the proposed method is used the hierarchical B-picture approach to classify video packets into different priorities. In the cross-layer optimization, the scalable video and packet classifier are proposed at the application layer, and the packet priority and queue allocation are used at the MAC layer. Finally, it is shown by NS2 simulations that the proposed method provides more stable wireless bandwidth and also achieves a better quality of the video streaming delivery.

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.

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