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| Approach | QoE monitoring point | QoE estimation point | QoE management applicability | Measurements environment | Subjective/Objective | Type of service | Deployment challenges |
|
| 3GPP 26.247 [74] | End user device (technical data) | Network | (i) Dynamic adaptation of service delivery to meet access network capabilities
(ii) Applicable in the context of optimization of limited network resource management | Real | Objective | Dynamic adaptive streaming over HTTP | (i) QoE service differentiation and prioritization
(ii) Battery consumption
(iii) Scalability
(iv) Computational complexity
(v) Data integrity
(vi) User's privacy issues |
| Ketykó et al. [88] | End user device (technical and user data) | End user device | Applicable in the context of application design improvement (better understanding of content effect) | Semi-real life | Both | Mobile YouTube video streaming | (i) Scalability
(ii) User's fairness/correctness
(iii) Feedback the estimation output to optimize the network
(iv) Data integrity |
| Wac et al. [35] | End user device (technical, user, and context data) | End user device | Applicable in the context of application design improvement | Real | Both | Wide range of mobile applications | (i) User's fairness/correctness
(ii) User's privacy issues
(iii) Computational complexity
(iv) Feedback the estimation output to optimize the network |
| Volk et al. [59] | Network | Network | (i) Applicable in the context of service estimation and optimization
(ii) Applicable in the context of the network resource allocation optimization | Real | Objective | SDE | (i) User subjectivity
(ii) Computational complexity |
| Varela and Laulajainen [91] | End user device | End user device | (i) Applicable in the context of service estimation and optimization
(ii) Applicable in the context of QoE-driven access network selection | Laboratory | Both | Mobile VoIP | (i) Tighter integration with the MIP software
(ii) Cost and battery consumption
(iii) User's fairness |
| Hoßfeld et al. [96] | End user device and network | Network | Applicable in the context of network resource optimization | Laboratory | Objective | YouTube video streaming | (i) User's privacy and subjectivity
(ii) Limitation of scalability
(iii) Additional costs due to DPI |
| Menkovski et al. [38] | Network | End user device | (i) Applicable in the context of network resource management
(ii) Applicable in the context of content encoding management | Laboratory | Both | Commercial mobile TV | (i) Inability to give information on service perception
(ii) Computational complexity |
| Staehle et al. [97] | End user device (technical data) and network | Network | (i) Applicable in the context of radio resource management
(ii) Applicable in the context of service degradation | Laboratory | Objective | YouTube video streaming | (i) Cross-layer information extraction
(ii) User's privacy issues
(iii) Battery consumption |
| Ketykó et al. [98] | End user device (technical and user data) and network | End user device | Applicable in the context of service estimation and optimization | Semi-real life | Both | Mobile video streaming | (i) Scalability
(ii) User's fairness
(iii) Feedback the estimation output to the network optimization
(iv) Battery consumption |
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