Abstract
Out of the large number of multimedia content sharing platforms
YouTube is the most popular one. This is reflected
by the large number of studies which focus on analyzing
YouTube characteristics. Techniques for quantifying the instantaneous
YouTube QoE and predicting an imminent QoE
degradation have in contrast never been proposed. The latter
task is even more important if network management actions
shall be carried out to avoid a YouTube QoE degradation.
In this work we describe YoMo, a tool which constantly
monitors the YouTube application comfort. This measure
quantifies the application operation condition and allows a
QoE prediction. Experiments show that YoMo is able to exactly
anticipate an upcoming YouTube QoE degradation.
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