In 2021, people spent about 20% of their daily time using smartphone apps. The smartphone has become essential for decision-making, entertainment, and communication, especially when mobile. The quality of these activities is influenced by the user's context and network state. Quality of Service (QoS) has been used to study user experience in relation to network metrics like jitter, packet loss, and throughput. However, the complexity of modern smartphone apps and users' rapidly changing environments have made QoS insufficient. Thus, Quality of Experience (QoE) was introduced, focusing on the human element of the system, including user intentions and expectations.

Current QoE research mainly improves network protocols or adapts content to various network situations, often in lab settings with limited participation and duration. There's a gap in understanding the long-term, real-world human factors affecting QoE. To address this, a study was conducted in natural settings, examining connectivity levels and application usage patterns among different user groups. This included a forecasting model based on crowdsourced app usage history to predict app launches, aiming to enhance smartphone app QoE.

The thesis proposed a method to quantify and improve smartphone app QoE by focusing on the context. A mixed-method approach was used to create a QoE quantifying model and a contextual model for predicting expected QoE. Additionally, a QoE notification system was tested, showing potential to reduce user annoyance and time spent on smartphones, laying groundwork for future QoE-enabled services.