Prof. Dr. Tobias Hoßfeld

Chair of Modeling of Adaptive Systems (MAS)Institute for Computer Science and Business Information Systems (ICB)University of Duisburg-Essen

www.mas.wiwi.uni-due.de

Can context monitoring improve QoE? A case study of video flash crowds in the Internet of Services

Hossfeld, Tobias; Skorin-Kapov, Lea; Haddad, Yoram; Pocta, Peter; Siris, Vasilios A.; Zgank, Andrej; Melvin, Hugh

Definition of Context and Context Influence Factors

• Context is any information that assists in determining a situation(s) related to a user, network or device.[A.K. Dey and G.D. Abowd. Toward a better understanding of context and context-awareness, Technical Report Georgia Institute of Technology]

• Context refers to anything that can be used to specify or clarify the meaning of an event. [P. Reichl et al, Towards a comprehensive framework for QoE and user behavior modelling, QoMEX 2015]

• Context influence factors are factors that embrace any situational property to describe the user’s environment in terms of physical, temporal, social, economic, task, and technical characteristics. [U. Reiter et al, Factors influencing quality of experience. In Quality of Experience, pp. 55-72. Springer International Publishing, 2014.]

or system.

or system‘s

Context Monitoring and QoE Monitoring

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Context monitoring

QoEmonitoring

e.g. devicecapabilities e.g. video

buffer status

e.g. user expectations

e.g. predicted traffic demands

e.g. available resources

e.g. QoS

utilization of data

Is context monitoring more relevant than QoE monitoring

for managing QoE?

Context Factors

• Physical environment in which services and devices are used.– home, office, commuting, and other places, – indoors vs outdoors.

• Social environment– service consumption e.g. alone, with an important person, with a group of friends, or in a

public place (consider gaming, watching video), – popularity of contents.

• Economic context– price for service consumption, tariff model: time, volume, flat– costs

• System context– load of system– system offloading possible, e.g. wifi offloading

• Usage context– Goal, task of service consumption, e.g. information retrieval vs. time killing– background vs. foreground application

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Examples• Follow the moon:

temporal and economic context

• Video streaming: physical context

• Video flash crowds: social context

Agenda

• Context monitoring and QoE monitoring

• Example use case: video flash crowds

• QoE model for HTTP adaptive streaming

• Numerical results

• Open issues: realization

SIMULATION MODEL

HTTP Adaptive Streaming

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Content Delivery with a CDN

Core network

Accessnetwork

Contentserver

ClientsCDNserver

Edge Content Delivery Network

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$$$

Global CDN Backbone$$$

Access Provider

Access Provider

TransitProvider

Point of PresencePoint of

Presence

Point of Presence

Point of Presence

$$$

Edge Cache

Simulation Scenario: Video Flash Crowd

• Video player– playout threshold of 6s – video stalls for empty buffer

• Video contents– Segment size of 2s– Two quality layers

• Flash crowd arrivals– users arrive– Exponential distributed interarrival times with rate – P(T<90s) = 99.27%,

• HAS algorithm• CDN load balancing

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CDN 1

CDN 2

Flash Crowd

ISP

bottleneck

HAS Algorithm and CDN Load Balancing

• CDN load balancing strategies1. CDN directs the first users to CDN 1, subsequent users

are assigned to CDN 2. Second, the CDN.2. Context monitoring based on information about the

flash crowd from a third party. Users are assigned to the CDN with the lowest number of users.

• HTTP adaptation strategy1. Actual buffer and throughput of last segment to

determine quality level of next segment2. Additional context information on number of users and

capacity per CDN3. Non-adaptive streaming algorithm: high quality level

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Bit rate

Time

TCP throughput

Requested chunks

SIMPLE QOE MODEL FOR HTTP ADAPTIVE STREAMING

What is the influence of stalling on Video QoE?

IQX-Hypothesis

ExcellentGoodFairPoorBad

54321

ImperceptiblePerceptible

Slightly annoyingAnnoying

Very annoying

• Small number of interruptions strongly affect YouTube QoE

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

0 1 2 3 4 5 61

2

3

4

5

number of stallings

MO

S

crowdsourcinglaboratory

QoE ( x )=α e−βx+γ

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Survey: Subjective Studies on HAS QoE

• Seufert, M.; Egger, S.; Slanina, M.; Zinner, T.; Hoßfeld, T.; Tran-Gia, P., "A Survey on Quality of Experience of HTTP Adaptive Streaming," Communications Surveys & Tutorials, IEEE , vol.17, no.1, pp.469,492, 2015doi: 10.1109/COMST.2014.2360940

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HTTP Adaptive

Streaming

Video Quality

Human Computer Interaction

Networking

etc.

Switching Frequency vs. Time on Layer

• In several works, switching frequency is reported to influence QoE

• Often parameters „number/frequency of switches“ and „time on layer“ are correlated and change simultaneously

• Keeping „time on layer“ constant no influence of switching frequency could be found

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Simple QoE Model for Two Quality Layers

• Simple QoE model based on two key influence factors• IQX provides a very good fit to the data points (R²=0.98)

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IQX-Hypothesis

Combined QoE Model

• Quality Adaptation Model– Based on time t on high layer– following IQX hypothesis

• Stalling Model– Based on number of stalls– following IQX hypothesis

• HTTP Adaptive Streaming Model

– Model still follows IQX hypothesis

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IQX-Hypothesis

NUMERICAL RESULTS:VIDEO FLASH CROWDS

Simulation Results

• No context information is used – CDN load balancing strategy: K=13– HAS quality

adaptation mechanism.

• CDN1 can serve13 / 35 users in high / low quality

• CDN2 can serve10 / 26 users inhigh / low quality

• Reaction too slow

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CDN Load Balancing Strategy

• Static assignment cannot achieve optimum

• Reactive approachbased on contextinformation improvesQoE for all users

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Summary of Results: CDN and HAS

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Bit rate

Time

TCP throughput

Requested chunks

Conclusions

• Context monitoring complements QoE monitoring– Utilization of additional information– Different types of context may be monitored

• Example of video flash crowds– Performance and QoE gain significantly improves– Technical realization needs to be developed

• Realization of context monitoring

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Realization of Context Monitoring using Social Data

• Accessing data from third party:Internet of Services

• Social data has to be monitored– Scale (single user, selected users,

all users)– Period (every hour, once a day, …)– Source (Online Social Networks OSNs,

Services, Service Providers, ISPs,…)

online social network

http://www.smartenit.eu

Content ProviderISPs

CDNs

$$$

$$$ $$$

$$$

AdsData

analysis

…

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Reseach Qestions: Social Data Monitoring

• Example: How to access data from OSNs?

•Design questions– Identification of relevant social data– Access method– Sampling strategy (scale, period, source,…)– Incentives (if necessary)

Method Information PredictionOSN collaboration All information Global, DetailedEnd user grants access to his data

Private information about end user and shared information about friends

Local, Detailed

Crawling/Sampling Public information Global, Vague

THANKS