Buffest

Krishnamoorthi, V. Ram; Carlsson, Niklas; Halepovic, Emir; Petajan, E.

doi:10.1145/3083187.3083193

Cited by 47 publications

(5 citation statements)

References 45 publications

(49 reference statements)

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“…However, similar to [112], it can generate significant overhead and is not resilient to agent failures. To reduce buffer underrun events and improve the client's viewing experience, Krishnamoorthi et al [114] presented BUFFEST, a classification framework for realtime prediction of the client's buffer conditions from both HTTP and HTTPS traffic. It consists of an event-based buffer emulator module and an automated training online classifier that are responsible for accurately tracking/predicting the client's buffer conditions and TCP/IP packet-level traffic classification, respectively.…”

Section: Network-assisted Adaptationmentioning

confidence: 99%

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Bentaleb

Taani

Begen

et al. 2019

IEEE Commun. Surv. Tutorials

349

157

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In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer's QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.

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Section: Network-assisted Adaptationmentioning

confidence: 99%

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Bentaleb

Taani

Begen

et al. 2019

IEEE Commun. Surv. Tutorials

349

157

View full text Add to dashboard Cite

show abstract

“…Here, authors also used machine learning as a promising technique for large-scale quality monitoring and prediction. [20] focuses on the reconstruction of buffered playtime at the video player side, as previously done in [7], but for encrypted network traffic. This is leveraged to estimate video QoE metrics in [21].…”

Section: Related Workmentioning

confidence: 99%

Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic

Seufert

Casas

Wehner

et al. 2019

2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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As stalling is the worst Quality of Experience (QoE) degradation of HTTP adaptive video streaming (HAS), this work presents a stream-based machine learning approach, ViCrypt, which analyzes stalling of YouTube streaming sessions in realtime from encrypted network traffic. The video streaming session is subdivided into a stream of short time slots of 1 s length, while considering two additional macro windows each for the current streaming trend and the whole ongoing streaming session. Constant memory features are extracted from the encrypted network traffic in these three windows in a stream-based fashion, and fed into a random forest model, which predicts whether the current time slot contains stalling or not. The presented system can predict stalling with a very high accuracy and the finest granularity to date (1 s), and thus, can be used in networks for real-time QoE analysis from encrypted YouTube video streaming traffic. The independent predictions for each consecutive slot of a streaming session can further be aggregated to obtain stalling estimations for the whole session. Thereby, the proposed method allows to quantify the initial delay, as well as the overall number of stalling events and the stalling ratio, i.e., the ratio of total stalling time and total playback time.

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“…Recently, the P.1203 model [15] has been standardized to predict the MOS (Mean Opinion Score) of HAS from stream inspection, thereby, also considering stalling. [16] focuses on the reconstruction of buffered playtime at the video player side, as previously done in [11]. It consists in a thresholdbased policy to notice gaps in the downloading of chunks and assumes that the player has moved to a new playback position.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [20] [21] relies on real cellular network measurements to predict typical QoE indicators for streaming services. [7] leverages the work in [16] to estimate video QoE metrics. [8] predicts initial delay, stalling, and video quality from the network traffic in windows of 10s.…”

Section: Related Workmentioning

confidence: 99%

Online Detection of Stalling and Scrubbing in Adaptive Video Streaming

Maggi

Leguay

Seufert

et al. 2019

2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT)

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Whether it is for network engineering or business intelligence insight purposes, it is crucial for an Internet Service Provider (ISP) to infer the Quality of Experience (QoE) perceived by the end user during a video streaming session. Specifically, it is important to detect video stalls as soon as they occur, to rapidly take counter-measures such as re-allocating resources more fairly among users. Video stalls fall into two different classes: (i) those caused by poor network conditions and (ii) those caused directly by the user when scrubbing or dragging the video playback forwards or backwards. However, only the former type of stalls degrade the QoE perceived by the end user. Therefore, in this paper we propose a technique to detect and classify stall events by observing the packets associated to a streaming session. We solve a least squares problem to minimize the distance between the estimated chunk's bitrate and the potential bitrate sequence that a plausible playback buffer dynamics would produce. This amounts to finding the maximally likely state sequence for a properly defined Hidden Markov Model. We propose two polynomial dynamic programming algorithms, one of which running in online fashion, computing the exact solution in the ideal case of complete and exact measurement set. We claim that our method is also applicable in an encrypted scenario, since it is robust with respect to the estimation error of a number of parameters, as we show via simulations.

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Buffest

Cited by 47 publications

References 45 publications

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic

Online Detection of Stalling and Scrubbing in Adaptive Video Streaming

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