To enable monitoring in a decentralized way with limited resources available in the last mile, a lightweight approach without unmanageable overhead is important to improve current services. In this scenario, given the enormous number of different streaming sessions and the associated humongous global data exchange, the analysis of every video stream at packet level cannot be carried out. Furthermore, the increasing load and different volumes of flows, and consequently the processing power required to monitor each flow, make detailed prediction even more complex, especially for a centralized monitoring entity. It is therefore necessary to predict quality by other flow monitoring and prediction techniques. However, since most of the data traffic is encrypted these days, in-depth monitoring with deep packet inspection is no longer possible for an ISP to determine crucial streaming related quality parameters. Intelligent and predictive service and network management is becoming more important to guarantee good streaming quality and meet user demands. This affects the overall streaming QoE for all end users, and ultimately the streaming providerвЂ™s revenue from long-term user churn.ДЇrom the perspective of an Internet Service Provider (ISP), responsible for network monitoring, the goal is to satisfy their customers and operate economically. Due to the increasing demand, streaming platforms like YouTube and Netflix have had to throttle the streaming quality in Europe in order to enable adequate quality for everybody on the Internet. The initial playback delay, streaming quality, quality changes, and video rebuffering events are the most important influencing factors. This development finds resonance in the May 2020 Sandvine report, which revealed that global Internet traffic was dominated by video, gaming, and social usage in particular, with these accounting for more than 80% of the total traffic, with YouTube hosting over 15% of these volumes.ДЇor video streaming, the Quality of Experience (QoE) is the most significant metric for capturing the perceived quality for the end user. Throughout the last year in particular, work, social, and leisure behaviors have changed rapidly towards the digital world. The ongoing trend in social life to often use a virtual environment is accelerated by the COVID-19 pandemic. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app.

The challenge for providers and operators nowadays is that existing approaches require large amounts of data. Current monitoring solutions are based on a variety of different machine learning approaches. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Streaming video is responsible for the bulk of Internet traffic these days.

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