  Unraveling quality of experience (QoE) of video streaming is very challenging
in bandwidth shared wireless networks. It is unclear how QoE metrics such as
starvation probability and buffering time interact with dynamics of streaming
traffic load. In this paper, we collect view records from one of the largest
streaming providers in China over two weeks and perform an in-depth measurement
study on flow arrival and viewing time that shed light on the real traffic
pattern. Our most important observation is that the viewing time of streaming
users fits a hyper-exponential distribution quite well. This implies that all
the views can be categorized into two classes, short and long views with
separated time scales. We then map the measured traffic pattern to bandwidth
shared cellular networks and propose an analytical framework to compute the
closed-form starvation probability on the basis of ordinary differential
equations (ODEs). Our framework can be naturally extended to investigate
practical issues including the progressive downloading and the finite video
duration. Extensive trace-driven simulations validate the accuracy of our
models. Our study reveals that the starvation metrics of the short and long
views possess different sensitivities to the scheduling priority at base
station. Hence, a better QoE tradeoff between the short and long views has a
potential to be leveraged by offering them different scheduling weights. The
flow differentiation involves tremendous technical and non-technical challenges
because video content is owned by content providers but not the network
operators and the viewing time of each session is unknown beforehand. To
overcome these difficulties, we propose an online Bayesian approach to infer
the viewing time of each incoming flow with the "least" information from
content providers.
