  HTTP Adaptive Streaming (HAS) is becoming the de-facto video delivery
technology over best-effort networks nowadays, thanks to the myriad advantages
it brings. However, many studies have shown that HAS suffers from many Quality
of Experience (QoE)-related issues in the presence of competing players. This
is mainly caused by the selfishness of the players resulting from the
decentralized intelligence given to the player. Another limitation is the
bottleneck link that could happen at any time during the streaming session and
anywhere in the network. These issues may result in wobbling bandwidth
perception by the players and could lead to missing the deadline for chunk
downloads, which result in the most annoying issue consisting of rebuffering
events. In this paper, we leverage the SoftwareDefined Networking paradigm to
take advantage of the global view of the network and its powerful intelligence
that allows reacting to the network changing conditions. Ultimately, we aim at
preventing the re-buffering events, resulting from deadline misses, and
ensuring high QoE for the accepted clients in the system. To this end, we use
Deterministic Network Calculus (DNC) to guarantee a maximum delay for the
download of the video chunks while maximizing the perceived video quality.
Simulation results show that the proposed solution ensures high efficiency for
the accepted clients without any rebuffering events which result in high user
QoE. Consequently, it might be highly useful for scenarios where video chunks
should be strictly downloaded on-time or ensuring low delay with high user QoE
such as serving video premium subscribers or remote control/driving of an
autonomous vehicle in future 5G mobile networks.
