  Mobile video consumption is increasing and sophisticated video quality
adaptation strategies are required to deal with mobile throughput fluctuations.
These adaptation strategies have to keep the switching frequency low, the
average quality high and prevent stalling occurrences to ensure customer
satisfaction. This paper proposes a novel methodology for the design of machine
learning-based adaptation logics named HASBRAIN. Furthermore, the performance
of a trained neural network against two algorithms from the literature is
evaluated. We first use a modified existing optimization formulation to
calculate optimal adaptation paths with a minimum number of quality switches
for a wide range of videos and for challenging mobile throughput patterns.
Afterwards we use the resulting optimal adaptation paths to train and compare
different machine learning models. The evaluation shows that an artificial
neural network-based model can reach a high average quality with a low number
of switches in the mobile scenario. The proposed methodology is general enough
to be extended for further designs of machine learning-based algorithms and the
provided model can be deployed in on-demand streaming scenarios or be further
refined using reward-based mechanisms such as reinforcement learning. All
tools, models and datasets created during the work are provided as open-source
software.
