%0 Conference Paper %1 info3-inproceedings-2019-28 %A Wassermann, Sarah %A Seufert, Michael %A Casas, Pedro %A Li, Gang %A Kuang, Li %B 4th Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE) %C Los Cabos, Mexico %D 2019 %K myown ua-net %T I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic %X We address the problem of real-time QoE monitoring of HAS, from the ISP perspective, focusing in particular on videoresolution analysis. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to predict different video resolution levels in a fine-grained scale, ranging from 144p to 1080p resolution, using as input only packet-level data. The proposed measurement system performs predictions in real time, during the course of an ongoing video-streaming session, with a time granularity as small as one second. We consider the particular case of YouTube video streaming. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements demonstrate that the proposed system can predict video resolution with very high accuracy, and in real time. Different from state of the art, the prediction task is not bound to coarse-grained video quality classes and does not require chunk-detection approaches for feature extraction.

@inproceedings{info3-inproceedings-2019-28, abstract = {We address the problem of real-time QoE monitoring of HAS, from the ISP perspective, focusing in particular on videoresolution analysis. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to predict different video resolution levels in a fine-grained scale, ranging from 144p to 1080p resolution, using as input only packet-level data. The proposed measurement system performs predictions in real time, during the course of an ongoing video-streaming session, with a time granularity as small as one second. We consider the particular case of YouTube video streaming. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements demonstrate that the proposed system can predict video resolution with very high accuracy, and in real time. Different from state of the art, the prediction task is not bound to coarse-grained video quality classes and does not require chunk-detection approaches for feature extraction.}, added-at = {2019-11-18T10:42:26.000+0100}, address = {Los Cabos, Mexico}, author = {Wassermann, Sarah and Seufert, Michael and Casas, Pedro and Li, Gang and Kuang, Li}, biburl = {https://www.bibsonomy.org/bibtex/2862c734aec6eb1bc0def0848dc6e415e/uniwue_info3}, booktitle = {4th Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE)}, interhash = {fe843ed4f10e62af93ef7b2537d5ebf6}, intrahash = {862c734aec6eb1bc0def0848dc6e415e}, keywords = {myown ua-net}, month = {10}, timestamp = {2022-03-14T00:13:59.000+0100}, title = {I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic}, year = 2019 }