ACQUA: A user friendly platform for lightweight network monitoring and QoE forecasting

Belmoukadam, Othmane; Spetebroot, Thierry; Barakat, Chadi

doi:10.1109/icin.2019.8685878

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Moreover, they showed that video streaming QoE is influenced by both the duration and the frequency of stalling events. On the other hand, we have mobile applications (e.g., ACQUA 6 ) able to infer QoE from network measurements made on the end users' mobile devices. The ACQUA application uses machine learning models calibrated with controlled experiments so as to capture the link between network level QoS (e.g., throughput, delay, and loss rate) and the objective QoE 6 …”

Section: Related Workmentioning

confidence: 99%

“…Even though 5G networks promise high connectivity and huge transmission capacity aiming to take the internet services and the corresponding user experience to the next level [3], [4], bandwidth sharing is still an important issue for network operators and content providers, especially in view of the exponential rise of video traffic volume ( Figure 1). It turns out that the objective aspect of Quality of Experience is tightly correlated to terminal playout characteristics (e.g., size, resolution) but also to network conditions [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

2020

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Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link MOS (Mean Opinion Score) to video bitrate for different screen types (e.g., Common Intermediate Format (CIF), Quarter Common Intermediate Format (QCIF), and High Definition (HD)). We leverage such studies and formulate a QoE driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE (Quality of Experience) over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and KKT (Karush Kuhn Tucker) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look-alike strategy implementing max-min fairness.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

2020

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“…In particular, the video streaming QoE is either dependent on the content itself (the video bitrate and resolution) or the application level QoS metrics such as start up delay, duration of stalls and resolution switches [8]- [12]. The latter application level metrics are proved to be tightly correlated to the network level QoS (e.g., throughput, delay and loss rate) [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

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

Belmoukadam¹,

Khokhar²,

Barakat³

2020

2020 11th International Conference on Network of the Future (NoF)

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Video streaming is, without a doubt, the most dominant application on the Internet. Each time a video streaming platform (e.g., YouTube, Dailymotion or Netflix) is requested, the browser loads a web page, setups the video player, then retrieves and renders the requested content. The video streaming transmission is based on the dynamic adaptive streaming over HTTP (DASH) which takes into consideration the underlying network conditions (e.g., delay, loss rate and throughput) and the terminal characteristics (viewport) to select the video resolution to request from the server. We question in this work the efficiency of this transmission in taking into account the terminal characteristics, the viewport in particular, knowing that requesting a resolution exceeding the viewport results in waste of bandwidth. Such bandwidth waste can either save money when the user is on a pay as you go data plane, or steal bandwidth from other users who are in need for it to further improve their Quality of Experience (QoE). To narrow the stats, we present a controlled experimental framework that leverages the YouTube and Dailymotion video players and the Chrome web request API to assess the impact of browser viewport on the observed video resolution pattern [1]-[3]. In a first attempt of kind, we use the observed patterns to quantify the amount of wasted bandwidth. Our data-driven analysis points to high sensitivity of the Dailymotion player toward small viewports (240x144 and 400x225) compared to the YouTube player resulting in 15% and 8% less bandwidth waste respectively. However, as the users shift toward large viewports, the YouTube player becomes more viewport friendly compared to the Dailymotion player with shows an estimated bandwidth waste of 28%.

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“…Previous studies manage to highlight some key QoE metrics such as stalls, average resolution, and representation fluctuations from measurements on the encrypted traffic and by using machine learning [10]. These studies mainly rely on the fact that application level metrics are proved to be tightly correlated to the network level QoS [11]- [13]. However, they overlook an important information regarding the resolution of the viewport on which the video is played out and its impact on the user QoE.…”

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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ACQUA: A user friendly platform for lightweight network monitoring and QoE forecasting

Cited by 7 publications

References 9 publications

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

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

From Encrypted Video Traces to Viewport Classification

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