  In this paper we consider the problem of adaptive video streaming over an
LTE-A network that utilizes Licensed Assisted Access (LAA) which is an instance
of LTE Unlicensed. With LTE Unlicensed users of the LTE-A network opportunisti-
cally access radio resources from unlicensed and licensed carriers through
Carrier Aggregation (CA). Our objective is to select the highest possible video
quality for each LTE-A user while also try to deliver video data in time for
playback, and thus avoid buffer under-run events that deteriorate viewing
experience. However, the unpredictable nature of the wireless channel, as well
as the unknown utilization of the unlicensed carrier by other unlicensed users,
result in a challenging optimization problem. We first focus on developing an
accurate system model of the adaptive streaming system, LTE-A, and the
stochastic availability of unlicensed resources. Then, the formulated problem
is solved in two stages: First, we calculate a proportionally fair video
quality for each user, and second we execute resource allocation on a shorter
time scale compatible with LTE-A. We compare our framework with the typical
proportional fair scheduler as well as a state-of-the-art LTE-A adaptive video
streaming framework in terms of average segment quality and number of buffer
under- run events. Results show that the proposed quality selection and
scheduling algorithms, not only achieve higher video segment quality in most
cases, but also minimize the amount and duration of video freezes as a result
of buffer under-run events.
