QoE Prediction for Multimedia Services

2014 ◽  
Vol 4 (3) ◽  
pp. 44-64 ◽  
Author(s):  
Natalia Kushik ◽  
Jeevan Pokhrel ◽  
Nina Yevtushenko ◽  
Ana Cavalli ◽  
Wissam Mallouli
Keyword(s):  
Fuzzy Logic ◽  
Expert Systems ◽  
Quality Of Experience ◽  
Logic Circuit ◽  
Experimental Results ◽  
Learning Approaches ◽  
Multimedia Services ◽  
Prediction Ability ◽  
Self Learning

This paper is devoted to the problem of evaluating the quality of experience (QoE) for a given multimedia service based on the values of service parameters such as QoS indicators. This paper proposes to compare two self learning approaches for predicting the QoE index, namely the approach based on logic circuit learning and the approach based on fuzzy logic expert systems. Experimental results for comparing these two approaches with respect to the prediction ability and the performance are provided.

QoE Prediction for Multimedia Services

2015 ◽  
pp. 1784-1804
Author(s):  
Natalia Kushik ◽  
Jeevan Pokhrel ◽  
Nina Yevtushenko ◽  
Ana Cavalli ◽  
Wissam Mallouli
Keyword(s):  
Fuzzy Logic ◽  
Expert Systems ◽  
Logic Circuit ◽  
Experimental Results ◽  
Multimedia Service ◽  
Learning Approaches ◽  
Multimedia Services ◽  
Prediction Ability ◽  
Self Learning

This paper is devoted to the problem of evaluating the quality of experience (QoE) for a given multimedia service based on the values of service parameters such as QoS indicators. This paper proposes to compare two self learning approaches for predicting the QoE index, namely the approach based on logic circuit learning and the approach based on fuzzy logic expert systems. Experimental results for comparing these two approaches with respect to the prediction ability and the performance are provided.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

Optimizing User Quality of Experience through Overlay Routing, Bandwidth Management and Dynamic Trans-Coding

2012 ◽  
pp. 160-180
Author(s):  
Maarten Wijnants ◽  
Wim Lamotte ◽  
Bart De Vleeschauwer ◽  
Filip De Turck ◽  
Bart Dhoedt ◽  
...  
Keyword(s):  
Packet Loss ◽  
Quality Of Experience ◽  
Multimedia Data ◽  
Effective Bandwidth ◽  
Bandwidth Management ◽  
Multimedia Services ◽  
Overlay Routing ◽  
Last Mile ◽  
Time Critical

Accessing multimedia services via fixed and wireless networks has become common practice. These services are typically much more sensitive to packet loss, delay and/or congestion than traditional services. In particular, multimedia data is often time critical and, as a result, network issues are not well tolerated and significantly deteriorate the user’s Quality of Experience (QoE). Therefore, the authors propose a QoE optimization platform that is able to mitigate problems that might occur at any location in the delivery path from service provider to customer. More specifically, the distributed architecture supports overlay routing to circumvent erratic parts of the network core. In addition, it comprises proxy components that realize last mile optimization through automatic bandwidth management and the application of processing on multimedia flows. This paper introduces a trans-coding service for this proxy component which enables the transformation of H.264/AVC video flows to an arbitrary bitrate. Through representative experimental results, the authors illustrate how this addition enhances the QoE optimization capabilities of the proposed platform by allowing the proxy component to compute more flexible and effective bandwidth distributions.

QoE Measurements and Analysis for VoIP Services

2016 ◽  
pp. 285-308 ◽  
Author(s):  
Ramon Sanchez-Iborra ◽  
Maria-Dolores Cano ◽  
Salvador Moreno-Urrea ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Quality Of Service ◽  
Comparative Study ◽  
Quality Of Experience ◽  
Clear Relationship ◽  
Quality Level ◽  
Multimedia Services ◽  
Subjective Evaluations ◽  
Subjective Perspective ◽  
Network Metrics

Quality of Service (QoS) and Quality of Experience (QoE) are two different approaches that evaluate the quality level of a given service. In general, QoS models analyze objective network parameters, while QoE techniques focus on what the user actually perceives when consuming the service (subjective perspective). In this chapter, it is presented a comparative study of two voice codecs employed by two well-known VoIP applications, namely Skype and Jitsi, analyzing the provided quality from two perspectives: objective and subjective evaluations. To this end, several network metrics have been taken into account; in addition, the participants in this study completed different quality surveys in order to obtain their opinion about the evaluated services. Concretely, 60 quality tests with 60 subjects have been performed in a controlled wired scenario. The obtained results suggest a clear relationship between QoS and QoE. This chapter aims to present a detailed description about the process to perform comparative quality evaluations of multimedia services from both QoS and QoE perspectives.

Influence of virtual reality and 3D printing on architectural innovation evaluation based on quality of experience evaluation using fuzzy logic

2021 ◽  
Vol 40 (4) ◽  
pp. 8501-8509
Author(s):  
Yangxuan ◽  
Zhaoqianjing
Keyword(s):  
Virtual Reality ◽  
Fuzzy Logic ◽  
3D Printing ◽  
Virtual Environment ◽  
Construction Industry ◽  
Quality Of Experience ◽  
New Technologies ◽  
Architectural Innovation ◽  
Manufacture Engineering

In the last few years, the construction industry’s primary energydem and in developed economies accounted for 30%–40%, and technological innovation is considered to be an urgent need for the transformation and upgrading of the construction industry. New technological innovations are continually changing the way the construction industry is implemented. In the construction industry, artificial intelligence is beginning to change all aspects of the construction industry, in the field of equipment planning, overall layout, safety, management and other fields have caused many changes. The integration of new technologies has revolutionized the traditional construction industry, such as virtual reality technology and 3D printing technology. In this work, we propose a categorization for assessing Virtual reality and 3D printing of Architectural Innovation on the basis of quality of experience (QoE) metric Evaluation of virtual environment using Fuzzy Logic (FL) System. The simulation result is analysed based on the comparative analysis of user experience with Fuzzy logic estimation for perception of virtual environment. The result analysis validated that the proposed FL system replicates the user valuation for architecture innovation applications more precisely and accurately thus FL is efficient method for predicting the inclusive QoE of a virtual reality and 3D printing. This paper will recapitulate the expansion of the two techniques in the manufacture engineering, demeanor examination on the presentation of diverse skills in architectural innovation, as well as explain through real cases how these two techniques have an influence on the improvement of the manufacture engineering. Simultaneously, this paper discourses the limitations of technology mixing and put advancing opinions on the future development of the manufactureengineering.

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