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Cord-cutting has spread like wildfire as streaming video become commonplace in the Internet. This motivated intensive research in adaptive video streaming to improve its quality-of-experience (QoE) in the presence of network quality variations. Much of the existing research either employed dummy video contents or open source videos for QoE evaluation which may not capture the full spectrum of characteristics of real-world contents. This work fills this gap by developing a novel EmuStream platform based on real-world contents to enable realistic experiments and evaluation of any adaptive streaming algorithm in any streaming platform. First, EmuStream offers the largest (700+ titles) publicly available video bitrate trace dataset derived from real-world video contents. Second, we developed a mathematical model based on the dataset which can generate bitrate trace data for arbitrary target bitrates without actual …