Helping Hand or Hidden Hurdle: Proxy-Assisted HTTP-Based Adaptive Streaming Performance

Bergström

Proceedings of the 2013 ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking

et al. 2013

Self Cite

This paper presents the design, implementation, and validation of a novel system that supports streaming and playout of personalized, multi-path, nonlinear video. In contrast to regular video, in which the file content is played sequentially, our design allows multiple nonlinear video sequences of the underlying (linear) video to be stitched together and played in any personalized order, and clients can be provided multiple path choices. The design combines the ideas of HTTP-based adaptive streaming (HAS) and multi-path nonlinear video. Personalization of the content is achieved with the use of a customized metafile, which is downloaded separately from the underlying media and the manifest file that defines the HAS structure. An extension to the user interface allows path choices to be presented to and made by the user. Novel buffer management and prefetching policies are used to ensure seamless uninterrupted playback regardless of client path choices, even under scenarios in which clients defer their choices until the last possible moment. Our solution allows creative home users to easily create their own multi-path nonlinear video, opening the door to an endless possibility of new opportunities and media forms.

Section: Resultsmentioning

confidence: 99%

Empowering the creative user

Bergström

Proceedings of the 2013 ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking

et al. 2013

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“…There is much complementary work concerning video delivery from the cloud [55] and/or with proxy support [56]. Future work could concern exploiting proxy support and/or cloud-computing capabilities in delivery of multi-video stream bundles.…”

Section: Discussionmentioning

confidence: 99%

Optimized Adaptive Streaming of Multi-video Stream Bundles

Eager

et al. 2017

IEEE Trans. Multimedia

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Abstract-In contrast to traditional video, multi-view video streaming allows viewers to interactively switch among multiple perspectives provided by different cameras. One approach to achieving such a service is to encode the video from all of the cameras into a single stream, but this has the disadvantage that only a portion of the received video data will be used, namely that required for the selected view at each point in time. In this paper we introduce the concept of a "multi-video stream bundle" that consists of multiple parallel video streams that are synchronized in time, each providing the video from a different camera capturing the same event or movie. For delivery we leverage the adaptive features and time-based chunking of HTTPbased Adaptive Streaming (HAS), but now employing adaptation in both content and rate. Users are able to change their viewpoint on-demand and the client player adapts the rate at which data is retrieved from each stream based on the user's current view, the probabilities of switching to other views, and the user's current bandwidth conditions. A crucial component of such a system is the prefetching policy. For this we present an optimization model as well as a simpler heuristic that can balance the playback quality and the probability of playback interruptions. After analytically and numerically characterizing the optimal solution, we present a prototype implementation and sample results. Our prefetching and buffer management solution is shown to provide close to seamless playback switching when there is sufficient bandwidth to prefetch the parallel streams.

“…Finally, it should be noted that many techniques have been proposed for client-driven or server-assisted quality adaptation [4,24], and for network-assisted prioritization of lows [6,26] using SDN-based technologies such as OpenFlow [33]. Others have proposed network-assisted quality selection for HAS clients based on network-based monitoring [8], or used measurements to model and characterize user satisfaction when using online services [38].…”

Section: Related Workmentioning

confidence: 99%

Buffest

Proceedings of the 8th ACM on Multimedia Systems Conference

Halepovic

et al. 2017

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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 bufer conditions of streaming clients and use this information to help avoid stalls due to empty bufers. However, estimation of clients' bufer 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 bufer 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.This paper presents BUFFEST, a novel classiication framework that can be used to classify and predict streaming clients' bufer conditions from both HTTP and HTTPS traic. To illustrate the tradeofs between prediction accuracy and the available information used by classiiers, we design and evaluate classiiers of diferent complexity. At the core of BUFFEST is an event-based bufer emulator module for detailed analysis of clients' bufer levels throughout a streaming session, as well as for automated training and evaluation of online packet-level classiiers. We then present example results using simple threshold-based classiiers and machine learning classiiers that only use TCP/IP packet-level information. Our results are encouraging and show that BUFFEST can distinguish streaming clients with low bufer conditions from clients with signiicant bufer margin during a session even when HTTPS is used.