scholarly journals Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Muhammad Saleem ◽  
Yasir Saleem ◽  
H. M. Shahzad Asif ◽  
M. Saleem Mian
Keyword(s):  
Quality Of Experience ◽  
Performance Metrics ◽  
Optimal Solution ◽  
Content Delivery ◽  
Multimedia Streaming ◽  
Multimedia Content ◽  
Quality Level ◽  
Limited Bandwidth ◽  
Multimedia Content Delivery

The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over HTTP is an efficient scheme for bitrate adaptation in which video is segmented and stored in different quality levels. The multimedia streaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We have proposed an adaptive rate control using enhanced Double Deep Q-Learning approach to improve multimedia content delivery by switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly evaluated against state-of-the-art heuristic and learning-based algorithms. The performance metrics such as PSNR, SSIM, quality of experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which shows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm provides faster convergence to the optimal solution as compared to other algorithms considered in our work.

Experimentally driven quality of experience-aware multimedia content delivery in modern wireless networks

2015 ◽  
Vol 29 (14) ◽  
pp. 2065-2083
Author(s):  
Georgios Aristomenopoulos ◽  
Vassilios Kaldanis ◽  
Giannis Katsaros ◽  
Symeon Papavassiliou
Keyword(s):  
Wireless Networks ◽  
Quality Of Experience ◽  
Content Delivery ◽  
Multimedia Content ◽  
Multimedia Content Delivery

Quality of Experience in Mobile Peer-to-Peer Streaming Systems

2012 ◽  
pp. 196-241
Author(s):  
Florence Agboma
Keyword(s):  
Quality Of Experience ◽  
Business Models ◽  
Prediction Models ◽  
Content Delivery ◽  
Peer To Peer ◽  
Network Resource ◽  
Relative Contribution ◽  
Qos Parameters ◽  
Streaming Systems

This chapter considers the various parameters that affect the user’s Quality-of-Experience (QoE) in mobile peer-to-peer streaming systems, which are a form of content delivery network. Network and content providers do not necessarily focus on users’ QoE when designing the content delivery strategies and business models. The outcome of this is quite often the over-provisioning of network resources and also a lack of knowledge in respect to the user’s satisfaction. The focus is the methodology for quantifying the user’s perception of service quality for mobile video services and user contexts. The statistical technique of discriminant analysis is employed in defining prediction models to map Quality-of-Service (QoS) parameters onto estimates of the user’s QoE ratings. The chapter considers the relative contribution of the QoS parameters to predicting user responses. The chapter also demonstrates the value of the prediction models in developing QoE management strategies in order to optimize network resource utilization. To investigate the versatility of the framework, a feasibility study was applied to a P2P TV system. P2P systems continue to develop and as such, not a lot is known about their QoE characteristics, which situation this chapter seeks to remedy.

Quality of Experience Models for Multimedia Streaming

2010 ◽  
Vol 2 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Vlado Menkovski ◽  
Georgios Exarchakos ◽  
Antonio Liotta ◽  
Antonio Cuadra Sánchez
Keyword(s):  
Machine Learning ◽  
Quality Of Experience ◽  
Prediction Models ◽  
Multimedia Streaming ◽  
Accurate Estimation ◽  
Management Service ◽  
Subjective Data ◽  
Efficient Management ◽  
Learning Techniques

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience (QoE) is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

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