  Multimedia streaming over the Internet (live and on demand) is the
cornerstone of modern Internet carrying more than 60% of all traffic. With such
high demand, delivering outstanding user experience is a crucial and
challenging task. To evaluate user QoE many researchers deploy subjective
quality assessments where participants watch and rate videos artificially
infused with various temporal and spatial impairments. To aid current efforts
in bridging the gap between the mapping of objective video QoE metrics to user
experience, we developed DashReStreamer, an open-source framework for
re-creating adaptively streamed video in real networks. DashReStreamer utilises
a log created by a HAS algorithm run in an uncontrolled environment (i.e.,
wired or wireless networks), encoding visual changes and stall events in one
video file. These videos are applicable for subjective QoE evaluation mimicking
realistic network conditions.
  To supplement DashReStreamer, we re-create 234 realistic video clips, based
on video logs collected from real mobile and wireless networks. In addition our
dataset contains both video logs with all decisions made by the HASalgorithm
and network bandwidth profile illustrating throughput distribution. We believe
this dataset and framework will permit other researchers in their pursuit for
the final frontier in understanding the impact of video QoE dynamics.
