Inferring Streaming Video Quality from Encrypted Traffic

Bronzino, Francesco; Schmitt, Paul; Ayoubi, Sara; Martins, Guilherme; Teixeira, Renata; Feamster, Nick

doi:10.1145/3393691.3394194

Cited by 5 publications

References 5 publications

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Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Adarsh

Nekrasov

Paul

et al. 2021

Passive and Active Measurement

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The explosion of mobile broadband as an essential means of Internet connectivity has made the scalable evaluation and inference of quality of experience (QoE) for applications delivered over LTE networks critical. However, direct QoE measurement can be time and resource intensive. Further, the wireless nature of LTE networks necessitates that QoE be evaluated in multiple locations per base station as factors such as signal availability may have significant spatial variation. Based on our observations that quality of service (QoS) metrics are less time and resource-intensive to collect, we investigate how QoS can be used to infer QoE in LTE networks. Using an extensive, novel dataset representing a variety of network conditions, we design several state-of-the-art predictive models for scalable video QoE inference. We demonstrate that our models can accurately predict rebuffering events and resolution switching more than 80% of the time, despite the dataset exhibiting vastly different QoS and QoE profiles for the location types. We also illustrate that our classifiers have a high degree of generalizability across multiple videos from a vast array of genres. Finally, we highlight the importance of low-cost QoS measurements such as reference signal received power (RSRP) and throughput in QoE inference through an ablation study.

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Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Adarsh

Nekrasov

Paul

et al. 2021

Passive and Active Measurement

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SETA: Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

Choi

Wijesinghe

Kattadige

et al. 2020

2020 IEEE 45th Conference on Local Computer Networks (LCN)

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Drop the packets

Mangla

Halepovic

Zegura

et al. 2020

Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies

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Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask:"Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.

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Inferring Streaming Video Quality from Encrypted Traffic

Cited by 5 publications

References 5 publications

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts

SETA: Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

Drop the packets

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