Inclusion of User Behavior and Social Context Information in ML-based

QoE Prediction

Fatima Laiche

1 a

, Asma Ben Letaifa

2 b

and Taouﬁk Aguili

Communication Systems Laboratory, ENIT, University Tunis El Manar, Tunis, Tunisia

MEDIATRON LAB, SUPCOM, Carthage University, Tunis, Tunisia

Keywords:

Video Streaming, Inﬂuence Factors, QoE, Machine Learning, User Behavior, Context, User Engagement.

Abstract:

The widespread use of online video content in every area of the connected world increases the interest in

Quality of Experience (QoE). QoE plays a crucial role in the success of video streaming services. However,

QoE prediction is challenging as many compelling factors (i.e., human and context factors) impact the QoE

and QoE management solution often neglect the impact of social context and user behavior factors on the end-

user’s QoE. To address these challenges, we have developed a web application to conduct subjective study and

collect data from application-layer, user-level, and service-level. The collected data is then used as training set

for machine learning models including decision tree, K-nearest neighbor, and support vector machine for the

purpose of QoE prediction.

1 INTRODUCTION

With the expansion of video streaming services over

the internet, mobile video trafﬁc is predicted to reach

79 percent of the overall mobile trafﬁc according to

the Cisco Visual Networking Index (VNI) (Ericsson

Mobility Report., 2018). This exponential growth is

due to the ever-increasing popularity of video stream-

ing services. Content providers such as YouTube,

Amazon Prime, and Hulu make decisions on the re-

source allocation based on both operational costs and

user-perceived quality.

Quality of Experience (QoE) is used to enhance

the quality of a service based on taking the user’s

opinion into account and integrating objective Qual-

ity of Service (QoS) and subjective inﬂuencing fac-

tors. A common deﬁnition of QoE is cited by the Eu-

ropean Network on Quality of Experience as “QoE is

the degree of delight or annoyance of the user of an

application or a service, it results from the fulﬁllment

of his or her expectations with respect to the utility

and/or enjoyment of the application or service in the

light of the user’s personality and current state.” (Le

Callet et al., 2012). Based on the above deﬁnition,

QoE encompasses subjective aspects besides objec-

tive parameters. It is a multidisciplinary domain that

https://orcid.org/0000-0002-2752-1709

https://orcid.org/0000-0002-1527-2557

covers multiple topics such as engineering, social psy-

chology, computer science, etc. Consequently, this

variety of research aspects has raised the challenges

related to the management of QoE.

As investigating, selecting relevant QoE inﬂuenc-

ing factors is the ﬁrst step towards an accurate QoE

prediction model (Building function model to map

QoE to real number). Through the recent studies,

QoE modeling with inﬂuence factors including con-

text (physical location) and human factors (user en-

gagement, interests, user proﬁle) in adaptive video

streaming services have attracted attention. However,

with the ever-growing adoption of adaptive stream-

ing, solutions that rely on in-network measurements

to estimate QoE often neglect user behavior and so-

cial contextual factors and their impact on the per-

formance estimation as they idealize the user’s en-

vironment. A lot of research has depicted the in-

ﬂuence of application-level Key Performance Indica-

tors (KPIs) on the QoE of videos delivered via the

dynamic adaptive streaming over HTTP paradigm.

Client-side monitoring can provide the information

at the application layer and consequently identifying

QoE degradation and according to the analysis con-

ducted with Machine Learning (ML) algorithms (Re-

iter et al., 2014; Barakovic et al., 2019; Juluri et al.,

2015) found that speciﬁc application-level KPIs inﬂu-

ence the perceived quality: playout resolution, initial

delay, average bitrate, and stalling. The underlying

Laiche, F., Ben Letaifa, A. and Aguili, T.

Inclusion of User Behavior and Social Context Information in ML-based QoE Prediction.

DOI: 10.5220/0010606405970604

In Proceedings of the 16th International Conference on Software Technologies (ICSOFT 2021), pages 597-604

ISBN: 978-989-758-523-4

597

assumption is that ML-based QoE estimation could

be improved with the availability of additional data at

application-level information provided by the service

provider. We identify three main categories of QoE

inﬂuence factors that would reﬂect users’ perceived

quality:

System-related: information about application-

speciﬁc parameters such as: buffering, stalling, initial

delay, etc.,

User behavior-related: indicates information about

user engagement (e.g., time spent watching) and user

interaction with the video player (e.g., number and

duration of pauses).

Social context-related: information about the popular-

ity (e.g., number of views) of videos and users’ pref-

erences (e.g., likes, dislikes).

We put emphasis on to what extent social context

and user behavior information could improve QoE es-

timation and what are the factors that have high inﬂu-

ence on the end-QoE this besides speciﬁc cause anal-

ysis and efﬁcient QoE management.

In this work, we choose YouTube social plat-

form for data gathering to obtain as much informa-

tion about the user-generated data on YouTube and to

show how exploiting social context and user behavior

information could improve the performance of QoE

estimation models.

To address this issue, we develop a crowdsourc-

ing framework to monitor and collect information

on video streaming services. The monitoring probe

is a web application that embeds YouTube videos,

we study the relation between application-level KPIs,

user behavior, and social contextual parameters, con-

duct features selection, and build a QoE model based

on machine learning to estimate QoE Mean Opinion

Score (MOS) based on highly correlated features re-

lated to objective and subjective aspects.

The remainder of this paper is structured as fol-

lows. Section 2 provides an overview of the state of

art on monitoring and estimating video QoE. Section

3 promotes our contributions. Section 4 reports on

the QoE prediction algorithms and discusses models’

performance results. Conclusion and perspectives are

discussed in Section 5.

2 RELATED WORK

To develop an efﬁcient QoE model it is crucial to

identify which factors cause network degradation and

inﬂuence most perceived QoE. QoS ﬂuctuation can

occur in different parts of the video streaming trans-

mission chain. Encryption of video trafﬁc transmitted

over the internet is increasing. Thus, the monitoring

solutions should be deployed at the client-side instead

of different parts on the network (e.g. user equip-

ment, home/access network, core network). To moni-

tor video streaming service on the user-side, (Wamser

et al, 2015) developed a passive android application

to measure the application level KPIs (i.e., video res-

olution, buffer, bit rate) of YouTube videos in the

user’s mobile device, another application for mo-

bile services called YOUQMON (Casas et al., 2013)

was proposed to estimate MOS of YouTube videos

in 3G networks from passively analyzing the trafﬁc.

Stalling events are calculated in real-time using the

QoE model and projected to MOS values. In the

same vein, (Maggi et al., 2019) proposed a technique

to detect stall events and classify them into stalling

caused by poor network quality or user-seeking inter-

actions. It is important to identify root cause anal-

ysis to take immediate actions such as fair reallo-

cation of resources in case stalling caused by net-

work conditions. Hence, crowdsourcing is consid-

ered to enable new functionalities to subjective eval-

uation, it is more time and cost-effective, ﬂexible and

it creates realistic test environment, One initiative in

the direction of collecting wide-scale video stream-

ing usage information for popular video streaming

services (e.g. YouTube, Amazon Prime Video, Net-

ﬂix) (Robitza et al., 2020) have developed a web

browser extension called YTCrowdMon. In most re-

cent research, the potential of ML techniques has been

exploited for KPI and QoE estimation of encrypted

trafﬁc from network/application-level metrics. (Di-

mopoulos et al., 2016) developed an ML-based model

to detect QoE degradation from encrypted trafﬁc.

They used the Random Forest algorithm to classify

video streaming sessions regarding three metrics that

inﬂuence adaptive video streaming QoE. i.e. stalling,

the average video quality, and quality variations. In a

more similar approach, in (Orsolic et al., 2017) a sys-

tem called YouQ is developed to monitor application-

level quality features and corresponding trafﬁc traces

per video session to classify YouTube videos using

various models into three QoE classes (“low, medium,

high”, and “low, high”). Instead of technical indi-

cators, there is another inﬂuence metric that can be

deemed worthwhile, indirect metadata of different

sources.

Contextual factors can reveal an important amount

of information on the quality of the transmission

chain and content of interest. Also, User engagement

which describes the viewing time of video stream-

ing has been used as a replacement for subjective

QoE. (Moldovan and Metzger., 2016) investigated the

correlation between QoE and user engagement for

on-demand video streaming services, and the results

ICSOFT 2021 - 16th International Conference on Software Technologies

598

show a strong correlation. A large-scale study was

conducted in (Shaﬁq et al., 2014) to characterize mo-

bile video streaming performance and study how user

engagement impacts to network and application Key

video performance metrics from the perspective of

a network operator. They proposed a model to pre-

dict video abandonment based on a strong correlation

observed between several network features and aban-

donment rate. Other metrics that depict social context

factors include popularity, the number of like-dislike,

and the number of comments. These features are usu-

ally applied on a video level and contribute to the

quality perceived by users. (Wu et al., 2018) inves-

tigated the relationship between the videos with high

engagement and view count using large-scale mea-

surements of YouTube metadata videos collected over

two months, they found engagement metrics more sta-

ble and predictable compared to popularity metrics

which are driven by external promotion. In a simi-

lar work, (Park et al., 2016) demonstrated a positive

correlation between video watching, comment senti-

ment, and popularity metrics. In a realistic environ-

ment, users interact with video players while watch-

ing videos, examples of user interactions are pausing,

playing, seeking, change display quality. (Seufert et

al., 2019) incorporated user interactions when build-

ing ML models for QoE analysis of encrypted traf-

ﬁc in real-time. While (Bartolec et al., 2019) have

trained ML models on data with and without user

playback related interactions to investigate how user

interaction could impact QoE/KPI estimation.

3 METHODOLOGY

3.1 Dataset and Crowdsourced

Measurement

The goal is to provide a measurement tool to mon-

itor speciﬁc Key Performance Indicators (KPIs) that

correlate with the perceived quality during a YouTube

session. We build a web QoE monitoring applica-

tion for YouTube crowdsourced QoE measurements.

The tool displays the same functionality as the native

YouTube client app. It gathers information from user-

level, service-level, and application-level using APIs

(e.g., Google, YouTube Data) and built-in functions.

Figure 1 shows the testbed used for data collection.

One hundred individuals participated in the experi-

ment. The participants were randomly selected from

SUP COM school within a university context. They

had invited other people who had prior experience

using a web application to conduct the experiments.

Written instructions were provided. They are asked

to insert their personal information: age, gender, pref-

erences to create a session. Afterward, they watch

videos according to their preferences and rate the per-

ceived quality multiple times. Figure 1 illustrates the

diagram of the QoE experiment testbed.

Figure 1: Proposed QoE forcasting methodology.

The dataset we collected consists of application-

level features, social context parameters, and user

behavior over videos. Table 1 depicts the metrics

reported by the monitoring app. The monitored

application-level data are the most relevant KPIs for

QoE in HTTP adaptive video streaming as initial de-

lay, buffering, stalling, and quality changes (Wamser

et a., 2015). We captured data that is related to user

behavior. User engagement describes the percentage

of the video viewed. We addressed one type of user

interaction with the video player which is the user-

initiated action to pause the video. Social context fac-

tors cover popularity and preferences data extracted

from YouTube metadata information (e.g., number of

views, like and dislike, etc.). To label the played

videos with MOS, the participants were asked to rate

the perceived quality.

We collected 1200 data records for four months con-

taining the subjective and objective measures (Table

1). to create a homogeneous dataset from crowd-

sourcing tests we ﬁltered out the invalid measure-

ments and unreliable instances. After cleaning the

data set we obtained 1010 instances each contains 20

features.

With the new dataset, we can ﬁgure out what impact

QoE and build a model based on ML algorithms that

predict accurate MOS values.

3.2 Dataset Characteristics

To study the impact of social context indicators and

user interactions on the end-users QoE we analyzed

the collected data. Figure 2 describes the distribution

of the relevant features for the quality assessment. It

Inclusion of User Behavior and Social Context Information in ML-based QoE Prediction

599

Table 1: Main features of the dataset.

Category Features

Application-level

information

Initial delay, etc.

Buffer level, avg buffer

level, etc.

Stalling, total stalling

length etc.

Quality switches, Avg

bitrate etc.

QoE rating MOS value, content

rating

User behavior

Watch time ratio.

Pause events

Social context View count,

Like/dislike count,

Category title

Figure 2: Relevant features.

shows that the users prefer to watch highly viewed

videos which led to higher content ratings and better-

quality ratings, they tend to give high rates to videos

with a large view count and that conﬁrms the positive

correlation between content popularity and MOS con-

sidering network transmission. Also, a negative rela-

tionship exists between pause events and MOS (Qual-

ity rating). In most of the videos viewed, we reported

the occurrence of stalling events because of drastic

bandwidth changes. We identiﬁed a correlation be-

tween user engagement and quality rating given the

fact that a previous study (Park et al., 2016) has iden-

tiﬁed this relation. Thus, we observed that the more

the user spent time watching more the MOS rating

is high. To understand the relationship between mul-

tiple features, Figure 3 shows the distribution of the

overall stalling and pause events, it can be noted that

in most videos when stalling happens, users tend to

pause the video for buffering to improve the stream-

ing quality. Figure 3 conﬁrms that stalling impacts

perceived quality as the users give a low rating when

stalling occurs. In the end, we can assume that user

behavior, user interaction, and content-related infor-

mation inﬂuence the perceived quality as much as the

stalling.

3.3 Data Preprocessing

In this section, we conduct feature selection and

feature importance to help build an efﬁcient ML

model. The model training component takes relevant

application-level, service-level, and user-level data as

input data. First, the former data is extracted using

APIs and fed to the feature selection algorithm to se-

lect the exact features and labels to be used for the

training of ML models. The collected data is used

as input to the feature selection algorithm to reduce

complexity and improve the performance of the pre-

dictive models by eliminating irrelevant features from

the dataset.

The pertinent features to be utilized are speciﬁed by

the feature selection algorithm. In our work, we relied

on the univariate selection method SelectKBest pro-

vided by scikit-learn library. This method enables the

selection of k highest scoring features that have the

strongest relationship with the output feature based

on univariate statistical tests. The algorithm reduced

the number of features to 14, they are listed in Figure

4. The most commonly selected variables are related

to features from different levels. The selected features

from the application level are mostly the features that

were selected in other work (Abar et al., 2017; Casas

et al., 2013; Ben Letaifa., 2018; Ben Letaifa., 2019)

and inﬂuence on the end QoE. Our ﬁndings show sim-

ilar results presented in the research area. In the case

of popularity metrics and user behavior and regard-

less of feature selection algorithm popularity metrics

have been proved to be highly related and inﬂuence

the perceptual quality. Speciﬁc user behavioral and

engagement metrics were also selected by the selec-

tion algorithm as they have a relationship with the per-

ceived quality.

To highlight which features may be most relevant to

the output MOS, we used two commons classiﬁers

Random Forest (RF) and XGB Classiﬁer to provide

an estimation of feature importance for a predictive

modeling problem. XGB algorithm decides on fea-

tures’ importance based on how many times that fea-

ture is used to make key decisions across all the trees.

RF as opposed to XGB method evaluate relative im-

ICSOFT 2021 - 16th International Conference on Software Technologies

600

(a) stalling/MOS. (b) Pause events/Stalling events.

Figure 3: Data Visualization.

portance scores for each feature separately. Figure 4

shows results in terms of feature importance with the

two used algorithms. In subﬁgures, 4a and 4b the im-

portance of features in the feature list differs. It can

be observed that features with less important scores

are different but still relevant such as popularity in-

dicators. Although, important features are almost the

same depending on whether RF or XGB was utilized.

The user behavior (pause events) and popularity indi-

cators contribute equally to the model performance.

To highlight which features may be most relevant to

the output MOS, we used two common classiﬁers

Random Forest (RF) and XGB Classiﬁer to provide

an estimation of feature importance for a predictive

modeling problem. XGB algorithm decides on fea-

tures importance based on how many times that fea-

ture is used to make key decisions across all the trees.

RF as opposed to XGB method evaluate relative im-

portance scores for each feature separately. Figure 4

shows results in terms of feature importance with the

two used algorithms. In subﬁgures 4a and 4b, the im-

portance of features in feature list differs. It can be

observed that features with less important scores are

different but still relevant such as popularity indica-

tors, although important features are almost the same

depending on whether RF or XGB was utilized. The

user behavior (pause events) and popularity indicators

contribute equally to the model performance.

3.4 Model Training

In this section, we conduct a feature selection study

to select pertinent features and we build a machine

learning model that predicts accurately QoE. to de-

ﬁne our model, we go with step 1: we use cross-

validation method to split the dataset into three sub-

sets: training, validation, and testing. This method

is more effective than splitting the dataset in training

and test data because it prevents problems like over-

ﬁtting. In step 2: to predict MOS using the most used

prediction models in literature: K Nearest Neighbor,

decision tree, and random forest. we applied a set

of hyperparameters aiming to ﬁnd the right combi-

nation of values that can help maximize the accu-

racy. In step 3, we evaluate the performance of our

models by measuring accuracy, Pearson correlation,

and F1- score. We end up by selecting the algo-

rithm that gives us the best prediction of MOS. We

build two ML-based models to show how social con-

textual info and user interactions enhance the perfor-

mance of QoE estimation. The models are trained on:

Model1: Application-level data, user engagement in-

dicator Model2: Application-level data, user behavior

info, and social contextual factors. Training ML mod-

els using dataset plays a crucial role to understand

the mathematical endeavor and create the right out-

put. In this work, we use algorithms: Random Forest,

K-Nearest Neighbor (KNN), and Decision Tree (DT),

description of the former ML algorithms and train-

ing phase will be discussed in the following. KNN

solves regression and classiﬁcation problems by clas-

sifying data points based on similarity measures, it

categorizes data regarding the classes of their clos-

est neighbors. to train KNN model, Euclidean met-

ric with equal distance weights has been selected as

the distance metric, and to avoid overﬁtting, a cross-

validation technique has been used when using differ-

ent values of K. the performance of KNN has been

presented in Table 2. Random forest inspired by the

DT algorithm; it is a special case of bootstrap bagging

applied to the decision tree. For model training and

from the literature we found the family of tree-based

algorithms perform best (Bartolec et al., 2019; Orso-

lic et al., 2017; Wamser et al., 2015). For the case of

in-network applications, we argue to use decision tree

and random forest as it requires fewer resources and

Inclusion of User Behavior and Social Context Information in ML-based QoE Prediction

601

(a) Features selected, XGB model. (b) Features selected, RF model.

Figure 4: Features importance using classiﬁcation models.

exhibits good performance.

4 EXPERIMENTAL RESULTS

4.1 Performance Evaluation Metric

In this section, we provide a description of per-

formance evaluation metrics. For objective quality

assessment, we use statistical metrics that cover

aspects: linearity, accuracy, and consistency against

subjective data. The statistical evaluation metrics we

used are summarized as follows:

Accuracy The accuracy is the fraction of the number

of correct predictions and it is deﬁned as (Eq.1):

where x

is the ground truth, and y

is the predicted

value, n denotes the number of samples.

A =

∑

i=1

f (x

, y

) (1)

F1 score measures the test accuracy it can be inter-

preted as the weighted mean of precision and recall,

it provides a standard scale of [0, 1] and F1 score

(Eq.2) reﬂects the robustness of the classiﬁer.

F1 =

TruePositives

TruePositives +

(FalsePositives + FalseNegatives)

(2)

Pearson Linear Correlation Coefﬁcient (PCC).

PCC measures the linear correlation between model’s

output and the subjective QoE. The range of PCC(Eq.

3) is [-1, 1] where 1 indicates positive correlation and

-1 negative correlation.

PCC =

∑

i=1

− x)(y

− y)

∑

i=1

− x)

∑

i=1

− y)

(3)

Yi denotes predicted MOS and xi the subjective one.

N indicates the total number of samples.

Table 2: Experiment results of Model2.

Classiﬁer Accuracy F1 Score

(macro-

avg)

PCC

RF 0.875 0.54 072

KNN

(neighbor

= 5)

0.77 0.27 0.56

KNN

(neighbor

= 10)

0.812 0.30 0.62

KNN

(neighbor

= 15)

0.812 0.30 0.62

DT 0.979 0.76 0.883

4.2 Evaluation

We evaluate the performance of QoE models. Table

2 and Table 3 show the performance of classiﬁcation

models. In the case of model1, our results show that

Decision tree gives the best results which makes it a

perfect candidate for QoE prediction system imple-

mentation. RF learning algorithm shows results better

than KNN although trying different values of neigh-

bors more than 10 didn’t enhanced the performance

of the algorithm. On the contrary, the model which

learns features from contextual, user behavior data be-

sides application KPIs performed better than model

trained only with application-level parameters. For

model2 DT algorithm shows the best results.

5 CONCLUSION

In this work, we presented a WebQoE monitoring ap-

proach and an applied test methodology based on real

time QoE estimation depicted by end users. based on

large dataset collected using WebQoE application a

solution has been proposed using decision tree clas-

ICSOFT 2021 - 16th International Conference on Software Technologies

602

Table 3: Experiment results of Model1.

Classiﬁer Accuracy F1 Score

(macro-

avg)

PCC

RF 0.790 0.47 061

KNN

(neighbor

= 5)

0.50 0.15 0.35

KNN

(neighbor

= 10)

0.612 0.20 0.35

KNN

(neighbor

= 15)

0.612 0.23 0.35

DT 0.779 0.56 0.653

siﬁer to predict QoE in the context of adaptive video

streaming services. For future work we aim to de-

velop QoE management approach of video services in

SDN/MEC environment where we can implement our

QoE prediction model as in-network solution. our on-

going work will focus on QoE management and con-

trol approach for video streaming services delivered

over the emergent network technologies.

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