Understanding the impact of network dynamics on mobile video user engagement

Shafiq, M. Zubair; Erman, Jeffrey; Ji, Lusheng; Liu, Alex X.; Pang, Jong-Shi; Wang, Jia

doi:10.1145/2637364.2591975

Cited by 77 publications

(62 citation statements)

References 22 publications

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“…[21]. (1) ads, (2) mixed HTTP streaming, (3) app store, (4) media optimization, (5) dating, (6) email, (7) games, (8) news info image media, (9) maps, (10) misc, (11) mms, (12) music audio, (13) p2p, (14) radio audio, (15) social network, (16) streaming video, (17) voip, (18) vpn, (19) web browsing/other http. For example, mixed HTTP streaming includes apps like YouTube, radio audio includes apps like TuneIn Radio, social network includes apps like Facebook, and music audio includes apps like Pandora.…”

Section: Data Setsmentioning

confidence: 99%

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Shafiq

Liu

et al. 2015

IEEE Trans. on Mobile Comput.

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Significant geospatial and temporal correlations, in terms of traffic volume and application access, exist in cellular network usage as shown in recent studies on cellular network measurement. Such geospatial and temporal correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained joint characterization of the geospatial and temporal dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial and temporal dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings for better network performance and user experience.

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Section: Data Setsmentioning

confidence: 99%

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Shafiq

Liu

et al. 2015

IEEE Trans. on Mobile Comput.

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“…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

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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“…In addition, also other Quality of Experience aspects besides energy consumption of mobile video have been studied. For example, Shafiq et al [35] report on a large scale measurement study of mobile video usage.…”

Section: Related Workmentioning

confidence: 99%

Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming

Siekkinen

Hoque

Nurminen

2016

IEEE/ACM Trans. Networking

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Video streaming can drain a smartphone battery quickly. A large part of the energy consumed goes to wireless communication. In this article, we first study the energy efficiency of different video content delivery strategies used by service providers and identify a number of sources of energy inefficiency. Specifically, we find a fundamental tradeoff in energy waste between prefetching small and large chunks of video content: small chunks are bad because each download causes a fixed tail energy to be spent regardless of the amount of content downloaded, whereas large chunks increase the risk of downloading data that user will never view because of abandoning the video. Hence, the key to optimal strategy lies in the ability to predict when the user might abandon viewing prematurely. We then propose an algorithm called eSchedule that uses viewing statistics to predict viewer behavior and computes an energy optimal download strategy for a given mobile client. The algorithm also includes a mechanism for explicit control of traffic overhead, i.e., unnecessary download of content that the user will never watch. Our evaluation results suggest that the algorithm can cut the energy waste down to less than half compared to other strategies. We also present and experiment with an Android prototype that integrates eSchedule into a YouTube downloader.

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Understanding the impact of network dynamics on mobile video user engagement

Cited by 77 publications

References 22 publications

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Buffest

Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming

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