Characterizing Client Behavior of Commercial Mobile Video Streaming Services

Mansy, Ahmed; Ammar, Mostafa; Chandrashekar, Jaideep; Sheth, Anmol

doi:10.1145/2594449.2579469

Cited by 23 publications

(18 citation statements)

References 7 publications

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“…Adaptive bitrate streaming is much-hyped across industry for switching the video quality in midstream [41,79,80,81,82,83,84,85], without any potential re-buffering and application restarting. YouTube has gone through a paradigm shift for switching progressive downloads towards adaptive streaming [86] in its desktop player in recent years.…”

Section: Towards Adaptive Streaming and Challengesmentioning

confidence: 99%

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Survey of Transportation of Adaptive Multimedia Streaming service in Internet

Taha

Lloret

Cánovas

et al. 2017

NPA

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Streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users experience. Due to changing network conditions, bit rate and initial delay, the multimedia file freezes or provides poor video quality to the end users. Researchers across industry and academia have explored HTTP Adaptive Streaming (HAS), which splits the video content into multiple segments and offers the clients varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively vary bit rate and video quality to match the transmission media conditions. Furthermore, the main aim of this paper is to give an overview on the state of the art of HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering, etc. It also focuses on various challenges on QoE influence ISSN 1943-3581 2017 www.macrothink.org/npa 86 factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Moreover, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments. Network Protocols and Algorithms

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Section: Towards Adaptive Streaming and Challengesmentioning

confidence: 99%

“…In recent years, YouTube and other regular services, such as Netflix, have enforced DASH as the favored streaming technology instead of FLV (Flash Video) streaming [83,84,86,87,88]. Of late, YouTube embraced HTML5 as video playback technique, which can completely abolish Flash.…”

Section: Towards Adaptive Streaming and Challengesmentioning

confidence: 99%

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

Taha

Lloret

Cánovas

et al. 2017

NPA

View full text Add to dashboard Cite

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“…YouTube (and other HAS players) sometimes re-download chunks at a higher quality [28,39]. In these cases, our emulator module (optimistically) assumes that the player always plays the chunk at the highest quality available at the player at the time the chunk is about to be played.…”

Section: Emulating the Bufer At The Nicmentioning

confidence: 99%

“…However, similar to the work by Dimopoulos et al [15], this approach facilitates identiication of events such as stalls only after they have occurred and does not identify or facilitate low-bufer conditions in real-time. Several other works have characterized the YouTube service itself [11,12,20], including the quality adaptation and redundant downloads [28,39].…”

Section: Related Workmentioning

confidence: 99%

Buffest

Krishnamoorthi

Carlsson

Halepovic

et al. 2017

Proceedings of the 8th ACM on Multimedia Systems Conference

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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.

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“…Pu et al [23] propose a proxy that can perform adaptation between wired and wireless networks to increase fairness for HTTP adaptive streaming to wireless clients. The importance of this work is demonstrated by Mansy et al [24], who evaluated mobile HTTP adaptive streaming to various mobile phone operating systems. They observed basic differences in delivering of the same service to different platforms and demonstrated that they lead to unfairness.…”

Section: Wireless Http Adaptivementioning

confidence: 99%

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

Evensen

Kupka

Riiser

et al. 2014

Advances in Multimedia

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Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects.

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Characterizing Client Behavior of Commercial Mobile Video Streaming Services

Cited by 23 publications

References 7 publications

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

Buffest

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

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