research-article

Watching user generated videos with prefetching

Authors:

Samamon Khemmarat,

Renjie Zhou,

Lixin Gao,

Michael ZinkAuthors Info & Claims

MMSys '11: Proceedings of the second annual ACM conference on Multimedia systems

Pages 187 - 198

https://doi.org/10.1145/1943552.1943578

Published: 23 February 2011 Publication History

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Abstract

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by showing that video playbacks of videos of YouTube is often unsatisfactory and introduce a series of prefetching schemes: the conventional caching scheme, the search result-based prefetching scheme, and the recommendation-aware prefetching scheme. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates a strong potential for improving the playback quality at the client. We also explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

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Cited By

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Li CZhao HZhao YZhang BLi C(2022)Joint Transcoding- and Recommending-Based Video Caching at Network EdgesIEEE Systems Journal10.1109/JSYST.2021.312448116:3(4928-4937)Online publication date: Sep-2022
https://doi.org/10.1109/JSYST.2021.3124481
Ansari NHan T(2017)ReferencesGreen Mobile Networks10.1002/9781119125099.refs(279-297)Online publication date: 25-Mar-2017
https://doi.org/10.1002/9781119125099.refs
Delbruel SFrey DTaiani F(2016)Exploring the Use of Tags for Georeplicated Content Placement2016 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E.2016.37(172-181)Online publication date: Apr-2016
https://doi.org/10.1109/IC2E.2016.37
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Index Terms

Watching user generated videos with prefetching
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. General and reference
  1. Cross-computing tools and techniques
    1. Design

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Reviews

Reviewer: Guenter Haring

The experience of video playback from user-generated video sharing sites suffers from interruptions and delays due to necessary buffering. The authors of this paper present the results of a study using prefetching when watching user-generated videos, which are generally rather short (three to ten minutes). In a brief introductory section, the authors describe the background and motivation of their work as well as the basic idea of their approach; the major results are summarized. As a motivation in section 2, user experiences with watching YouTube videos are analyzed. The analysis is based on video download traces, which are investigated using the Wireshark network protocol analyzer. The analyzed data represent downloads from 12 different locations and access technologies. From each location, ten videos of different quality levels were downloaded. The presented results comprise: the number of interrupted playbacks, the number of pauses in the interrupted playbacks, and the amount of time that a user had to wait. Motivated by section 2's results, in section 3, the authors present their video prefetching scheme, where the prefetching agent (PA) can be located either at the client or at the proxy. In the first case, it receives requests only from one client. The PA stores prefetched prefixes of videos and can perform caching. To determine the set of videos to be prefetched, the authors propose two possible algorithms: one based on users' search results, and one that uses the YouTube recommendation system. The advantage of both approaches is that they are simple and not computationally expensive. In section 4, the authors describe the necessary data collection, which is composed of two phases: the data traffic between a campus network and YouTube servers, and the retrieval of two lists (search result and related video lists) from YouTube using the YouTube Data application programming interface (API). The process is described in detail. Section 5 presents the evaluation of the video prefetching approaches, where the two selection algorithms and two settings (client versus proxy) are compared. To evaluate the approaches, the authors perform a trace-driven simulation based on actual user usage patterns. In the evaluation, two metrics are used: the hit ratio and precision. The first gives the fraction of requests that can be served from the prefetching storage, and the second gives the accuracy of the video selection algorithm (the fraction of the requested videos over the total number of prefetched videos). The experiments are performed both in the case when the PA always has sufficient storage space and when it has only limited storage space. In the sufficient storage case, the algorithm based on the related video lists in combination with the proxy setting gives the best hit ratio (up to almost 76 percent), which is also influenced by the fact that users in the same local network share similar interests. The authors also investigate how many requests from each referrer are hit when the related video lists are used for prefetching, both for the proxy and the client setting. The results show that there is overlap between the videos shown in the related video lists and videos requested through other referrers; a detailed analysis is given. This analysis also shows the advantage of using the related video lists for selecting the videos to be prefetched. The authors also present the combination of the prefetching approach with caching. The results are improved by five to 20 percent compared to the prefetch-only mode. Finally, the authors show that the storage required to achieve the highest hit ratio is within feasible range for a campus network. In section 6, the authors discuss the influence of limited storage space and the choice of the number of videos to be prefetched from each related video list. Furthermore, the question of how large the prefetched prefix should be, as well as various aspects of the general feasibility of prefetching are discussed. The paper finishes with sections on related work, conclusions, and references. The paper is well done, despite several typographical errors and some annoying inaccuracies. Online Computing Reviews Service

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Published In

MMSys '11: Proceedings of the second annual ACM conference on Multimedia systems

February 2011

294 pages

ISBN:9781450305181

DOI:10.1145/1943552

General Chairs:
Ali C. Begen
Cisco, Canada
,
Ketan Mayer-Patel
University of North Carolina, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 February 2011

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MMSYS '11: MMSYS '11 - Multimedia Systems Conference

February 23 - 25, 2011

CA, San Jose, USA

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Overall Acceptance Rate 176 of 530 submissions, 33%

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Li CZhao HZhao YZhang BLi C(2022)Joint Transcoding- and Recommending-Based Video Caching at Network EdgesIEEE Systems Journal10.1109/JSYST.2021.312448116:3(4928-4937)Online publication date: Sep-2022
https://doi.org/10.1109/JSYST.2021.3124481
Ansari NHan T(2017)ReferencesGreen Mobile Networks10.1002/9781119125099.refs(279-297)Online publication date: 25-Mar-2017
https://doi.org/10.1002/9781119125099.refs
Delbruel SFrey DTaiani F(2016)Exploring the Use of Tags for Georeplicated Content Placement2016 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E.2016.37(172-181)Online publication date: Apr-2016
https://doi.org/10.1109/IC2E.2016.37
Sharma NKrishnappa DBarker SIrwin DShenoy P(2015)Managing server clusters on intermittent powerPeerJ Computer Science10.7717/peerj-cs.341(e34)Online publication date: 9-Dec-2015
https://doi.org/10.7717/peerj-cs.34
FUKUMOTO NANO SGOTO S(2015)Behavior Analysis of Video Application Users on Smart Phones Based on State Transition DiagramIEICE Transactions on Communications10.1587/transcom.E98.B.42E98.B:1(42-50)Online publication date: 2015
https://doi.org/10.1587/transcom.E98.B.42
Krishnappa DZink MGriwodz CHalvorsen P(2015)Cache-Centric Video RecommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/271631011:4(1-20)Online publication date: 2-Jun-2015
https://dl.acm.org/doi/10.1145/2716310
Ali-Eldin AKihl MTordsson JElmroth EOoi WFeng WLiu F(2015)Analysis and characterization of a video-on-demand service workloadProceedings of the 6th ACM Multimedia Systems Conference10.1145/2713168.2713183(189-200)Online publication date: 18-Mar-2015
https://dl.acm.org/doi/10.1145/2713168.2713183
Stanescu AMarcu M(2015)Adaptive video streaming solution for varying mobile networks environments2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics10.1109/SACI.2015.7208240(417-421)Online publication date: May-2015
https://doi.org/10.1109/SACI.2015.7208240
Paul TPuscher DWilk SStrufe T(2015)Systematic, large-scale analysis on the feasibility of media prefetching in Online Social Networks2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)10.1109/CCNC.2015.7158072(755-760)Online publication date: Jan-2015
https://doi.org/10.1109/CCNC.2015.7158072
Chakareski J(2014)Social video cachingSignal Processing: Image Communication10.1016/j.image.2014.02.00229:4(462-471)Online publication date: Apr-2014
https://doi.org/10.1016/j.image.2014.02.002
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