Adaptive video streaming over edge computing infrastructures offers a promising approach to improving user-perceived Quality of Experience (QoE) by reducing latency and enabling more efficient content delivery. However, traditional ABR (Adaptive Bitrate) algorithms often prioritize individual QoE maximization, potentially leading to unfair resource distribution across users with heterogeneous network conditions. In this work, we present a Fairness-Aware QoE Assessment framework for adaptive video streaming in edge-based architectures. Our testbed combines realistic network traces, group-based user separation, and multiple ABR strategies—including heuristic and Machine learning-based approaches to analyze the interplay between QoE performance and fairness. To enable a deeper evaluation, we adopt four complementary fairness indicators: QoE Fairness Score (QFS), Bitrate Fairness Index (BFI), Time-Fair QoE (TF-QoE), and Stability-Aware Fairness (SAF). This multi-metric approach provides greater granularity in identifying how different ABR strategies affect the overall user experience and resource allocation fairness in edge-assisted adaptive streaming environments. Experimental results show that the Machine Learning-based approach achieves the most balanced trade-off between QoE and fairness, outperforming heuristics particularly under heterogeneous network conditions. Moreover, fairness-specific metrics such as SAF and TF-QoE revealed disparities in playback stability and group-level equity that would remain hidden with QoE-only evaluations.