{'fraction_non_alphanumeric': 0.018590998043052837, 'fraction_numerical': 0.0, 'mean_word_length': 5.276073619631902, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Passive YouTube QoE Monitoring for ISPs

Over the last decade, Quality of Experience (QoE) has become the guiding paradigm for enabling a more user-centric understanding of quality of communication networks and services. The intensifying competition among ISPs and the exponentially increasing traffic volumes caused by online video platforms like YouTube is forcing service providers to integrate QoE into their corporate DNA. This paper investigates the problem of YouTube QoE monitoring from an access provider's perspective. To this end, we present three novel methods for in-network measurement of the QoE impairment that dominates user perception in the context of HTTP video-streaming: stalling of playback. Our evaluation results show that it is possible to detect application-level stalling events at high accuracy by using network-level passive probing only. However, only the most complex and most accurate approach can be used for QoE prediction due to the non-linear ties inherent in human quality perception.