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

Realistic Video Sequences for Subjective QoE Analysis