  Quality of Experience~(QoE)-driven adaptive bitrate (ABR) algorithms are
typically optimized using QoE models that are based on the mean opinion
score~(MOS), while such principles may not account for user heterogeneity on
rating scales, resulting in unexpected behaviors. In this paper, we propose
Jade, which leverages reinforcement learning with human feedback~(RLHF)
technologies to better align the users' opinion scores. Jade's rank-based QoE
model considers relative values of user ratings to interpret the subjective
perception of video sessions. We implement linear-based and Deep Neural Network
(DNN)-based architectures for satisfying both accuracy and generalization
ability. We further propose entropy-aware reinforced mechanisms for training
policies with the integration of the proposed QoE models. Experimental results
demonstrate that Jade performs favorably on conventional metrics, such as
quality and stall ratio, and improves QoE by 8.09%-38.13% in different network
conditions, emphasizing the importance of user heterogeneity in QoE modeling
and the potential of combining linear-based and DNN-based models for
performance improvement.
