The introduction of new video coding tools aimed at providing support for various video types and the increasing popularity of six degrees of freedom (6DoF) based immersive media intended to further user experience, highlights the requirement for user based Quality of Experience (QoE) awareness for optimal user satisfaction. However, the effectiveness of such tools and media applications is influenced by disruptive factors that can impact QoE. This is present in the most recently adopted video coding standard, Versatile Video Coding (VVC), which provides considerable improvements compared to previous standards. Although efforts introduced improve compression efficiency, the standard does not account for the impact of erroneous network conditions on user QoE, in the encoding process, thus lacking QoE awareness for optimal user satisfaction. Likewise, the increasingly popular immersive media application, live holographic communication, which provides three dimensional (3D) photo-realistic representations of objects with 6DoF user movement for enhanced immersive experiences, encounters the same issue. Although measures have been introduced to mitigate QoE degradation, the impact of several factors extending beyond disruptive network conditions, such as user behaviour, object behaviour and network awareness are not accounted for. As such, video coding tools, and immersive media applications fail to adequately address the user awareness requirement as several QoE impactful factors are not properly factored in such tools or applications resulting in sub optimal user satisfaction.To address this, this thesis proposes various contributions to this effect. Firstly, extensive analysis on the influence of erroneous Quality of Service (QoS) parameters is presented, facilitating the generation of a VVC compatible QoE model and QoE aware framework for VVC based video transmission. Secondly, detailed evaluation and analysis on the influence of key factors and performance metrics relevant to user QoE is presented, preceding a proposed network compliant QoE model. Lastly, a novel intelligent multi-dimensional adaptation framework for immersive media applications is introduced. In this context, the impact of human-oriented and environment oriented factors regarding QoE performance metrics is explored and accounted for, with machine learning leveraged to provide satisfactory viewer experiences. Extensive experimentation and comparisons against the state of the art are conducted, with results indicating that the contributions provide enhanced user experiences.