Abstract
In the contemporary digital landscape, service and content providers are increasingly focused on delivering high-quality multimedia services, a commitment driven by escalating user expectations and the advent of technologies like ultra-high-definition video and interactive services. Despite considerable advancements and investments in enhancing service capabilities, the surging demand for video services presents a multifaceted challenge.The key to user retention and satisfaction lies in maintaining a consistent quality of service, a task complicated by the limitations of available bandwidth and the consequential network or service impairments. These factors significantly influence user experience, with the potential risk of increased user churn, which can negatively impact the economic sustainability of multimedia services. Therefore, user satisfaction has become a critical element in the service delivery ecosystem.Addressing this challenge necessitates a shift beyond traditional Quality of Service (QoS) metrics, which primarily focus on network parameters, to a more holistic approach that embraces Quality of Experience (QoE). This dissertation aims to investigate the effects of changes in video resolution on user satisfaction by employing ecologically valid subjective quality assessments and developing a predictive methodology for video streaming applications based on these insights.This research undertakes four systematically designed subjective quality assessments under controlled conditions, following ITU-T Rec. P.910 guidelines. These assessments involve 61 switching conditions and 8 diverse video contents, amounting to 345 test sequences and 32,775 individual test runs. The goal is to analyze the impact of different video encoding schemes and resolution changes on user QoE in video streaming services.The insights derived from these assessments have been pivotal in the development of an innovative predictive model. The model’s innovative aspect lies in the method of its development and the new parameters directly associated with user ratings that it utilizes. The model represents a significant advancement in the field, as it integrates cutting-edge modelling techniques and uniquely addresses the frequency of resolution changes. This advancement underscores the model’s potential to offer a more nuanced understanding of user preferences in the context of video streaming services. The model seeks to enhance the delivery of adaptive video streaming services by aligning with userpreferences, improving satisfaction on a targeted impact on video resolution changes.In conclusion, this research underscores the importance of a QoE-based approach, transcending the limitations of QoS-focused strategies. By conducting carefully designed subjective quality assessments and innovative modelling efforts, this dissertation provides valuable contributions to understanding the role of video resolution change impairments in user satisfaction for video streaming services, highlighting the pivotal role of usercentered approaches in the multimedia service industry.
Awarding Institution(s)
University of Plymouth
Supervisor
Lingfen Sun, Ali C. Begen, Is-Haka Mkwawa
Document Type
Thesis
Publication Date
2025
Embargo Period
2025-08-09
Deposit Date
August 2025
Additional Links
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Asan, A. (2025) Quality of Experience Assessment for Enhanced Video Streaming Services. Thesis. University of Plymouth. Available at: https://doi.org/10.24382/9t51-ms28
