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Follow the Crowd: On QoE for Internet Applications . Tobias Hoßfeld. www3.informatik.uni-wuerzburg.de www.t-hossfeld.de. What is the Internet crowd consuming ?. Web and Cloud Applications Online Video, Web Browsing, Downloads, Cloud Services, etc. Why relevant?

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Follow the crowd on qoe for internet applications

Follow the Crowd: On QoE for Internet Applications

Tobias Hoßfeld

www3.informatik.uni-wuerzburg.dewww.t-hossfeld.de


What is the internet crowd consuming
Whatisthe Internet crowdconsuming?

  • Web and Cloud Applications

    • Online Video, Web Browsing, Downloads, Cloud Services, etc.

  • Why relevant?

    • Constitute dominant internet use cases

    • Generate relevant share of network traffic

Global Consumer Internet Traffic Volume (Forecast).Source: Cisco VNI 2011.


YouTube QoEandPracticalGuidelines



Video transmission over the internet
Video Transmission over the Internet

  • UDP-based streaming

  • Unreliable transmission

  • Video quality affected

  • Artifacts may occur

  • Stimuli are visual degradationsor artifacts

  • HTTP streaming

  • Reliable transmission

  • Video quality not affected

  • But stalling may occur

  • Most stimuli/impairments are of temporal nature

  • YouTube uses HTTP streaming

  • Internet technology changes quality perception


Key influence factors on youtube qoe
Key Influence Factors on YouTube QoE

  • Interesting: no significantcorrelation of QoE and

    • initialdelay

    • video characteristics likeresolution, type of content,ratio of audio/video, etc.

    • users preference, whether they liked video

    • demographical features

  • Stalling frequency andstalling duration determinethe user perceived quality

  • Support vector machines and correlation coefficients


What is the influence of stalling on youtube qoe
What is the influence of stalling on YouTube QoE?

  • Small number of interruptions strongly affect YouTube QoE

  • Provider (i.e. content and network provider) must avoid stalling

  • Total stalling time not sufficient for good QoE estimation

  • Monitoring of QoErequires sophisticated methods to capture stalling pattern, e.g. using DPI or directly at end user


Provider between the devil and the deep blue sea
Provider: Between the Devil and the Deep Blue Sea?

  • In case of insufficient resources,

    • „one“ has to choose between initial delays and stalling

    • What is worse for users?

    • Stalling has to be avoided,even at costs of initial delays

  • Current work: Is YouTube QoEmanagement beneficial for ISPs?

  • Users do „QoE management“ themselves – by pausing the video to prefetch contents and then to consume w/o interruptions

  • ISP may „invest“ in capacity, sophisticated traffic management, e.g. DASH and SVC

  • Exponential increase of costs wrt. quantile (of video corpus)

  • Delivering videos with about 120% of video bitrateas “rule of thumb”


Crowdsourcing forQoETesting


Crowdsourcing
Crowdsourcing

  • Crowdsourcing is a neologism composed of “crowd“ and “outsourcing“  literally, it means outsourcing to a (large, anonymous) crowd

  • All tasks are web-based micro jobs, typically little effort to fulfill

  • Crowdsourcing interesting for (QoE) user studies

    • large user panel, diversity of users, international users,

    • user studies can be executed in short time,

    • low costs in contrast to laboratory studies,

    • QoE tests for Internet applications with realistic settings

Crowdsourcing is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call.

Jeff Howe - Definition of Crowdsourcing “The White Paper Version“


Crowdsourcing workflow
Crowdsourcing Workflow

1

2

  • Challenges due to remote setting

    • Unreliable QoEresults, no test moderator

    • Heterogeneousenvironment, devices, users

4

3

5


Countermeasures unreliability
Countermeasures: Unreliability

  • Proper Test Design and statistical methods for filtering data

    • Consistency Tests

      • “Same” question is asked multiple times in different manner.

      • Example: user is asked about his origin country in the beginning and about his origin continent at the end.

    • Content Questions

      • Simple questions about the video clip, after watching the video.

      • Example, “Which sport was shown in the clip? A) Tennis. B) Soccer. C) Skiing.‘”

      • Application Usage Monitoring

      • Example: measuring the time the worker spends on the task

      • Example: monitor browser events and user reactions

  • Utilizefeaturesof crowdsourcing platform

    • Specializedcrowds, whichhavecertainskills, reliability, etc.

    • Conducttrainingsessions, two-stage tests

    • Payment accordingtoquality


Lessons learned unreliable workers
Lessons Learned: Unreliable workers

  • FILTER LEVEL 1:

  • - wrong answers to content questions

  • different answers to the same questions

  • always selected same option

  • consistency questions: specified the wrong country/continent

FILTER LEVEL 2:

- did not notice stalling

- perceived non-existent stalling

  • Many user ratings rejected

  •  use simple testinstructions

  •  avoid Java applets

  • takecareoflow Internet speed

  •  avoid incentives for users to cheat, see Facebook results of student’s friends

  • User warning („Test not done carefully“)  rejection rate decreased about 50%

  • improvements possible  detailed analysis of (inter and intra-) rater reliability revealed: filtering too strict

FILTERLEVEL 3:

- did not watch all videos completely

C1 C2 C3 C4 C5 C6 C7 Facebook

First crowdsourcing tests


Crowdsourcing vs laboratory studies
Crowdsourcing vs. Laboratory Studies

  • Crowdsourcing testswith Microworkers.com at Uni Würzburg

  • Lab studies within ACE 2.0 at FTW’s i:Lab

  • Similar results in laboratory and crowdsourcing study

  • Crowdsourcing appropriateforQoEtestsof Internet apps

single stall event: 4 sec

videoduration: 30 sec

  • 2,035 users from more than 60 countries participated in tests and rated 8,163 video. Payment was below 200,- Euro.

  • User diversity

  • Statisticallysignificantresults

  • Lowcosts, fast conduction


Crowdsourcingtests for hd live streaming
CrowdsourcingTestsfor HD Live Streaming

  • Live video streaming investigated via Microworkers and Facebook

  • Joint work within QUALINET STSM by Bruno Gardlo “Improving Reliability for Crowdsourcing-Based QoE Testing”

  • Strong differences due to worse viewing conditions and smaller screen resolutions  context monitoring required, e.g. light conditions,

  • Critical, proper analysis of data, consider hidden influence factors


CurrentActivitiesin Qualinet


Qualinet crowdsourcing task force
Qualinet “Crowdsourcing“ Task Force

  • Goal

    • Derive a methodologyand setup for crowdsourcing in QoE assessment,

    • Challenge crowdsourcing QoE assessment approach with usual “lab” methodologies, comparisonof QoE tests

    • Develop mechanisms and statistical approaches for identifying reliable ratings from remote crowdsourcing users,

    • Define requirements onto crowdsourcing platforms for improved QoE assessment.

  • Experiences with crowdsourcing

    • What are the main challenges? Reliability, environment/context monitoring, technical implementation, language problems …

    • Cartography for crowdsourcing use cases and mechanisms

    • Database with crowdsourcing results, e.g. impact of context factors on QoE, country, habits, …

  • Framework for crowdsourcing QoE tests

    • Results are implemented in framework “QualityCrowd” by TU Munich

    • Further information: https://www3.informatik.uni-wuerzburg.de/qoewiki/qualinet:crowd


Qualinet task force web and cloud apps
Qualinet Task Force „Web andCloud Apps“

E-Mail

Instant Messaging

Customer Relationship Management

Office

Desktop

Gaming

  • Technology changeandservicemigrationtocloudsstronglyimpactsuserperceptionandQoE

  • Currentactivites

    • DropboxQoE and mulicollaboration tools

    • QoE-aware adaptation mechanism for video streaming: DASH and SVC

    • Standardization: finalization of model and measurement methodology for web browsing QoE

  • https://www3.informatik.uni-wuerzburg.de/qoewiki/qualinet:webcloud

Application Type

Example

OnLive

Google Mail

Facebook Chat

SalesForce.com

MS Office Live

EyeOS

Small

High

DegreeofInteractivity



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