  Quality of Experience (QoE) provisioning in heterogeneous access networks
(HANs) can be achieved via handoffs. The current approaches for QoE-aware
handoffs either lack the availability of a network path probing method or lack
the availability of efficient methods for QoE prediction. Further, the current
approaches do not explore the benefits of proactive QoE-aware handoffs such
that user's QoE is maximized by learning from past network conditions and by
actions taken by the mobile device regarding handoffs. In this paper, our
contributions are two-fold. First, we propose, develop and validate a novel
method for QoE prediction based on passive probing. Our method is based on
hidden Markov models and Multi-homed Mobility Management Protocol which
eliminates the need for additional probe packets for QoE prediction. It
achieves the average QoE prediction accuracy of 97%. Second, we propose,
develop and validate a novel reinforcement learning based method for proactive
QoE-aware handoffs. We show that our method outperforms existing approaches by
reducing the number of vertical handoffs by 60.65% while maintaining high QoE
levels and by extending crucial functionality such as passive probing
mechanisms.
