Uncertainty-aware robust adaptive video streaming with bayesian neural network and model predictive control

In this paper, we propose BayesMPC, an uncertainty-aware robust adaptive bitrate (ABR) algorithm on the basis of Bayesian neural network (BNN) and model predictive control (MPC). Specifically, to improve the capacity of learning transition probability of the network throughput, we adopt a BNN-based predictor that is able to predict the statistical distribution of future throughput from the past throughput by not only considering the aleatoric uncertainty (e.g., noise), but also capturing the epistemic uncertainty incurred by lack of adequate training samples. We further show that by using the negative log-likelihood loss function to train this BNN-based throughput predictor, the generalization error can be minimized with the guarantee of PAC-Bayesian theorem. Rather than a point estimate, the learnt uncertainty can contribute to a confidence region for the future throughput, the lower bound of which then leads to an uncertainty-aware robust MPC strategy to maximize the worst-case user quality-of-experience (QoE) w.r.t. this confidence region. Finally, experimental results on three real-world network trace datasets validate the efficiency of both the proposed BNN-based predictor and uncertainty-aware robust MPC strategy, and demonstrate the superior performance compared to other baselines, in terms of both the overall QoE performance and generalization across all ranges of heterogeneous network and user conditions.