  Accurate cross-layer information is very useful to optimize mobile networks
for specific applications. However, providing application-layer information to
lower protocol layers has become very difficult due to the wide adoption of
end-to-end encryption and due to the absence of cross-layer signaling
standards. As an alternative, this paper presents a traffic profiling solution
to passively estimate parameters of HTTP Adaptive Streaming (HAS) applications
at the lower layers. By observing IP packet arrivals, our machine learning
system identifies video flows and detects the state of an HAS client's
play-back buffer in real time. Our experiments with YouTube's mobile client
show that Random Forests achieve very high accuracy even with a strong
variation of link quality. Since this high performance is achieved at IP level
with a small, generic feature set, our approach requires no Deep Packet
Inspection (DPI), comes at low complexity, and does not interfere with
end-to-end encryption. Traffic profiling is, thus, a powerful new tool for
monitoring and managing even encrypted HAS traffic in mobile networks.
