  Mobile apps are increasingly relying on high-throughput and low-latency
content delivery, while the available bandwidth on wireless access links is
inherently time-varying. The handoffs between base stations and access modes
due to user mobility present additional challenges to deliver a high level of
user Quality-of-Experience (QoE). The ability to predict the available
bandwidth and the upcoming handoffs will give applications valuable leeway to
make proactive adjustments to avoid significant QoE degradation. In this paper,
we explore the possibility and accuracy of realtime mobile bandwidth and
handoff predictions in 4G/LTE and 5G networks. Towards this goal, we collect
long consecutive traces with rich bandwidth, channel, and context information
from public transportation systems. We develop Recurrent Neural Network models
to mine the temporal patterns of bandwidth evolution in fixed-route mobility
scenarios. Our models consistently outperform the conventional univariate and
multivariate bandwidth prediction models. For 4G \& 5G co-existing networks, we
propose a new problem of handoff prediction between 4G and 5G, which is
important for low-latency applications like self-driving strategy in realistic
5G scenarios. We develop classification and regression based prediction models,
which achieve more than 80\% accuracy in predicting 4G and 5G handoffs in a
recent 5G dataset.
