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BUFFEST: Predicting Buffer Conditions and Real-time Requirements of HTTP(S) Adaptive Streaming Clients
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
AT&T Labs, USA.
AT&T Labs, USA.
2017 (English)In: MMSys’17, Proceedings of the 8th ACM on Multimedia Systems Conference, ACM , 2017, p. 76-87Conference paper, Published paper (Refereed)
Abstract [en]

Stalls during video playback are perhaps the most important indicator of a client's viewing experience. To provide the best possible service, a proactive network operator may therefore want to know the buffer conditions of streaming clients and use this information to help avoid stalls due to empty buffers. However, estimation of clients' buffer conditions is complicated by most streaming services being rate-adaptive, and many of them also encrypted. Rate adaptation reduces the correlation between network throughput and client buffer conditions. Usage of HTTPS prevents operators from observing information related to video chunk requests, such as indications of rate adaptation or other HTTP-level information.; AB@This paper presents BUFFEST, a novel classification framework that can be used to classify and predict streaming clients' buffer conditions from both HTTP and HTTPS traffic. To illustrate the tradeoffs between prediction accuracy and the available information used by classifiers, we design and evaluate classifiers of different complexity. At the core of BUFFEST is an event-based buffer emulator module for detailed analysis of clients' buffer levels throughout a streaming session, as well as for automated training and evaluation of online packet-level classifiers. We then present example results using simple threshold-based classifiers and machine learning classifiers that only use TCP/IP packet-level information. Our results are encouraging and show that BUFFEST can distinguish streaming clients with low buffer conditions from clients with significant buffer margin during a session even when HTTPS is used.

Place, publisher, year, edition, pages
ACM , 2017. p. 76-87
Keywords [en]
Buffer condition estimation, HTTP-based adaptive streaming, HTTPS, Real-time requirements
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-140913DOI: 10.1145/3083187.3083193ISBN: 978-1-4503-5002-0 (electronic)OAI: oai:DiVA.org:liu-140913DiVA, id: diva2:1141611
Conference
8th ACM Conference on Multimedia Systems, June 20-23, 2017, Taipei, Taiwan
Available from: 2017-09-15 Created: 2017-09-15 Last updated: 2018-02-06
In thesis
1. Efficient HTTP-based Adaptive Streaming of Linear and Interactive Videos
Open this publication in new window or tab >>Efficient HTTP-based Adaptive Streaming of Linear and Interactive Videos
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Online video streaming has gained tremendous popularity over recent years and currently constitutes the majority of Internet traffic. As large-scale on-demand streaming continues to gain popularity, several important questions and challenges remain unanswered. This thesis addresses open questions in the areas of efficient content delivery for HTTP-based Adaptive Streaming (HAS) from different perspectives (client, network and content provider) and in the design, implementation, and evaluation of interactive streaming applications over HAS.

As streaming usage scales and new streaming services emerge, continuous improvements are required to both the infrastructure and the techniques used to deliver high-quality streams. In the context of Content Delivery Network (CDN) nodes or proxies, this thesis investigates the interaction between HAS clients and proxy caches. In particular, we propose and evaluate classes of content-aware and collaborative policies that take advantage of information that is already available, or share information among elements in the delivery chain, where all involved parties can benefit. Asides from the users’ playback experience, it is also important for content providers to minimize users’ startup times. We have designed and evaluated different classes of client-side policies that can prefetch data from the videos that the users are most likely to watch next, without negatively affecting the currently watched video. To help network providers to monitor and ensure that their customers enjoy good playback experiences, we have proposed and evaluated techniques that can be used to estimate clients’ current buffer conditions. Since several services today stream over HTTPS, our solution is adapted to predict client buffer conditions by only observing encrypted network-level traffic. Our solution allows the operator to identify clients with low-buffer conditions and implement policies that help avoid playback stalls.

The emergence of HAS as the de facto standard for delivering streaming content also opens the door to use it to deliver the next generation of streaming services, such as various forms of interactive services. This class of services is gaining popularity and is expected to be the next big thing in entertainment. For the area of interactive streaming, this thesis proposes, models, designs, and evaluates novel streaming applications such as interactive branched videos and multi-video stream bundles. For these applications, we design and evaluate careful prefetching policies that provides seamless playback (without stalls or switching delay) even when interactive branched video viewers defer their choices to the last possible moment and when users switches between alternative streams within multi-video stream bundles. Using optimization frameworks, we design and implement effective buffer management techniques for seamless playback experiences and evaluate several tradeoffs using our policies.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 72
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1902
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-143802 (URN)10.3384/diss.diva-143802 (DOI)9789176853719 (ISBN)
Public defence
2018-03-13, Ada Lovelace, B-huset, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Funder
CUGS (National Graduate School in Computer Science)Swedish Research Council
Available from: 2018-02-12 Created: 2018-02-06 Last updated: 2018-02-13Bibliographically approved

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