Analysis of YouTube’s traffic adaptation to dynamic environments

Añorga, Javier; Arrizabalaga, Saioa; Sedano, B.; Goya, Jon; Alonso-Arce, Maykel; Mendizábal, Jaizki

doi:10.1007/s11042-017-4695-9

Cited by 8 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While automation frameworks such as Selenium have enabled large scale measurements presented in related work [15], [17], [19], [21], [25], these can only be applied if viewing content in the browser. We are aware of mobile automation frameworks such as Appium 7 and plan to employ these in the future.…”

Section: Datasets and Addressed Research Topicsmentioning

confidence: 99%

“…The aforementioned standards define methods and formats, while each compliant service, on top of that, defines its own adaptation algorithm -chunk size, the amount of data that is kept in the buffer, etc. Various employed adaptation strategies have been analysed over the past years [7]- [10], but we note that these findings are susceptible to change and may become outdated, as service providers change their deployed strategies with newer service versions available on the market.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for in-Network QoE Monitoring of Encrypted Video Streaming

Oršolić

Skorin‐Kapov

2020

IEEE Access

View full text Add to dashboard Cite

With the amount of global network traffic steadily increasing, mainly due to video streaming services, network operators are faced with the challenge of efficiently managing their resources while meeting customer demands and expectations. A prerequisite for such Quality-of-Experience-driven (QoE) network traffic management is the monitoring and inference of application-level performance in terms of video Key Performance Indicators (KPIs) that directly influence end-user QoE. Given the persistent adoption of end-to-end encryption, operators lack direct insights into video quality metrics such as start-up delays, resolutions, or stalling events, which are needed to adequately estimate QoE and drive resource management decisions. Numerous solutions have been proposed to tackle this challenge on individual use-cases, most of them relying on machine learning (ML) for inferring KPIs from observable traffic patterns and statistics. In this paper, we summarize the key findings in state-of-the-art research on the topic. Going beyond previous work, we devise the concept of a generic framework for ML-based QoE/KPI monitoring of HTTP adaptive streaming (HAS) services, including model training, deployment, and re-evaluation. Components of the framework are designed in a generic way, independent of a particular streaming service and platform. The methodology for applying different framework components is discussed across various use-cases. In particular, we demonstrate framework applicability in a concrete use-case involving the YouTube service delivered to smartphones via the mobile YouTube app, as this presents one of the most prominent examples of accessing YouTube. We tackle both QoE/KPI estimation on a per-video-session level (utilizing the validated ITU-T P.1203 QoE model), as well as ''real-time'' KPI estimation over short time intervals. Obtained results provide important insights and challenges related to the deployment of a generic in-network QoE monitoring framework for encrypted video streams.INDEX TERMS Quality of Experience (QoE), video streaming, in-network QoE estimation, machine learning, encrypted traffic.

show abstract

Section: Datasets and Addressed Research Topicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for in-Network QoE Monitoring of Encrypted Video Streaming

Oršolić

Skorin‐Kapov

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…There is a flurry of academic ABR proposals [4,25,22,15,14,28,19,10,7,11,23,18], but only limited study of the large number of deployed video streaming platforms catering to varied video types and audiences. YouTube itself is relatively well studied, with several analyses of various aspects of its behavior [16,5,27], including video encoding, startup behavior, bandwidth variations at fixed quality, a test similar to our reactivity analysis, variation of segment lengths, and redownloads to replace already fetched segments. There is also an end-end analysis of Yahoo's video streaming platform using data from the provider [8].…”

Section: Related Workmentioning

confidence: 99%

Understanding Video Streaming Algorithms in the Wild

Licciardello

Grüner

Singla

2020

Passive and Active Measurement

View full text Add to dashboard Cite

While video streaming algorithms are a hot research area, with interesting new approaches proposed every few months, little is known about the behavior of the streaming algorithms deployed across large online streaming platforms that account for a substantial fraction of Internet traffic. We thus study adaptive bitrate streaming algorithms in use at 10 such video platforms with diverse target audiences. We collect traces of each video player's response to controlled variations in network bandwidth, and examine the algorithmic behavior: how risk averse is an algorithm in terms of target buffer; how long does it takes to reach a stable state after startup; how reactive is it in attempting to match bandwidth versus operating stably; how efficiently does it use the available network bandwidth; etc. We find that deployed algorithms exhibit a wide spectrum of behaviors across these axes, indicating the lack of a consensus one-size-fitsall solution. We also find evidence that most deployed algorithms are tuned towards stable behavior rather than fast adaptation to bandwidth variations, some are tuned towards a visual perception metric rather than a bitrate-based metric, and many leave a surprisingly large amount of the available bandwidth unused.

show abstract

“…An overview of the most important HAS algorithms is given in [7]. A detailed analysis of the adaptation of YouTube is given in [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS

Moldovan

Hagn

Sieber

et al. 2017

2017 29th International Teletraffic Congress (ITC 29)

View full text Add to dashboard Cite

Abstract-Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best.In this paper, we want to identify the trade-off between the average video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows us to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.

show abstract

Analysis of YouTube’s traffic adaptation to dynamic environments

Cited by 8 publications

References 19 publications

A Framework for in-Network QoE Monitoring of Encrypted Video Streaming

A Framework for in-Network QoE Monitoring of Encrypted Video Streaming

Understanding Video Streaming Algorithms in the Wild

Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS

Contact Info

Product

Resources

About