On excess bandwidth usage of video streaming: when video resolution mismatches browser viewport

Belmoukadam, Othmane; Khokhar, Muhammad Jawad; Barakat, Chadi

doi:10.1109/nof50125.2020.9249133

Cited by 2 publications

(2 citation statements)

References 20 publications

(18 reference statements)

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“…Authors in [17] argue that any representation exceeding the resolution of the viewport brings the maximum level of QoE. Moreover, in a previous work [18], we also observed that the DASH transmission process may result in download resolutions exceeding the screen resolution (i.e., a viewport in full-screen mode), hence resulting in a waste of bandwidth. Such waste can be of particular concern to end-users paying their subscription at the byte level and to operators who can invest it on other flows in need of it.…”

Section: Background and Motivationmentioning

confidence: 53%

Unveiling the End-User Viewport Resolution From Encrypted Video Traces

Belmoukadam

Barakat

2021

IEEE Trans. Netw. Serv. Manage.

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Video streaming is without doubt the most requested Internet service, and main source of pressure on the Internet infrastructure. At the same time, users are no longer satisfied by the Internet's best effort service, instead, they expect a seamless service of high quality from the side of the network. As result, Internet Service Providers (ISP) engineer their traffic so as to improve their end-users' experience and avoid economic losses. Content providers from their side, and to enforce customers privacy, have shifted towards end-to-end encryption (e.g., TLS/SSL). Video streaming relies on the dynamic adaptive streaming over HTTP protocol (DASH) which takes into consideration the underlying network conditions (e.g., delay, loss rate, and throughput) and the viewport capacity (e.g., screen resolution) to improve the experience of the end user in the limit of the available network resources. In this work, we propose an experimental framework able to infer fine-grained video flow information such as chunk sizes from encrypted YouTube video traces. We also present a novel technique to separate video and audio chunks from encrypted traces based on Gaussian Mixture Models (GMM). Then, we leverage our dataset to train models able to predict the class of viewport (either SD or HD) per video session with an average 92% accuracy and 85% F1-score. The prediction of the exact viewport resolution is also possible but shows a lower accuracy than the viewport class.Index Terms-Video streaming, controlled experiments, video chunk size, viewport resolution, YouTube encrypted traces, machine learning.

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Section: Background and Motivationmentioning

confidence: 53%

Unveiling the End-User Viewport Resolution From Encrypted Video Traces

Belmoukadam

Barakat

2021

IEEE Trans. Netw. Serv. Manage.

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“…Cermak et al [9] answered partially the question concerning the bandwidth needs for acceptable video experience on a set of screen resolutions. Still, downloading any resolution exceeding the screen resolution (or viewport) will be automatically downsized, hence resulting in a waste of bandwidth [14]. However, the literature is missing a solution to infer this screen resolution from passive captures of encrypted video traffic.…”

Section: Introductionmentioning

confidence: 99%

From Encrypted Video Traces to Viewport Classification

Belmoukadam¹,

Barakat²

2020

2020 16th International Conference on Network and Service Management (CNSM)

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The Internet has changed drastically in recent years, multiple novel applications and services have emerged, all about consuming digital content. In parallel, users are no longer satisfied by the Internet's best effort service, instead, they expect a seamless service of high quality from the side of the network. This has increased the pressure on Internet service providers (ISP) in their effort to efficiently engineer their traffic and improve their end-users' experience. Content providers from their side, and to further protect the content of their customers, have shifted towards end-to-end encryption (e.g., TLS/SSL), which has complicated even further the task of ISPs in handling the traffic in their network. The challenge is notable for video streaming traffic which is driving the Internet traffic growth, and which imposes tight constraints on the quality of service provided by the network depending on the content of the video stream and the equipment on the end-user premises. Video streaming relies on the dynamic adaptive streaming over HTTP (DASH) protocol which takes into consideration the underlying network conditions (e.g., delay, loss rate, and throughput) and the viewport capacity (e.g., screen resolution) to improve the experience of the end user in the limit of available resources. Nevertheless, knowing the reality of the encrypted video traffic is of great help to ISPs as it allows taking appropriate network management actions. In this work, we propose an experimental framework able to infer fine-grained video flow information such as chunk sizes from encrypted YouTube video traces. We also present a novel technique to separate video and audio chunks from encrypted traces based on Gaussian Mixture Models (GMM). We evaluate our technique with real chunk sizes (Audio/Video) collected through the browser using the Chrome Web Request API [1]. Then, we leverage these results and our dataset to train a model able to predict the class of viewport (either SD or HD) per video session with an average 92% accuracy and 85% F1 score.

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On excess bandwidth usage of video streaming: when video resolution mismatches browser viewport

Cited by 2 publications

References 20 publications

Unveiling the End-User Viewport Resolution From Encrypted Video Traces

Unveiling the End-User Viewport Resolution From Encrypted Video Traces

From Encrypted Video Traces to Viewport Classification

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