qoe model
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Author(s):  
Shengbin Meng ◽  
Minyin Zeng ◽  
Junlin Li ◽  
Yue Wang ◽  
Zongming Guo


Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 829
Author(s):  
Antonio J. García ◽  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez

In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.



Author(s):  
Huyen T. T. Tran ◽  
Nam Pham Ngoc ◽  
Truong Cong Thang
Keyword(s):  


Author(s):  
Stefan Uhrig ◽  
Sebastian Moller ◽  
Dawn M. Behne ◽  
U. Peter Svensson ◽  
Andrew Perkis


2019 ◽  
Vol 165 ◽  
pp. 106967
Author(s):  
H.-F. Bermudez ◽  
J.-M. Martinez-Caro ◽  
R. Sanchez-Iborra ◽  
J.L. Arciniegas ◽  
M.-D. Cano


2019 ◽  
Vol 11 (8) ◽  
pp. 171 ◽  
Author(s):  
Tho Nguyen Duc ◽  
Chanh Minh Tran ◽  
Phan Xuan Tan ◽  
Eiji Kamioka

The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user’s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.



2019 ◽  
Vol 78 (22) ◽  
pp. 31987-32006 ◽  
Author(s):  
Phisit Pornpongtechavanich ◽  
Therdpong Daengsi


Author(s):  
Abubkr Elmnsi ◽  
Fahad Mira ◽  
Niemah Osman ◽  
Is-Haka Mkwawa
Keyword(s):  


2019 ◽  
Vol 26 (2) ◽  
pp. 21-32 ◽  
Author(s):  
Longyu Zhang ◽  
Haiwei Dong ◽  
Abdulmotaleb El Saddik
Keyword(s):  


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