Improving QoE prediction in mobile video through machine learning

Casas, Pedro; Wassermann, Sarah

doi:10.1109/nof.2017.8251212

Cited by 19 publications

(5 citation statements)

References 20 publications

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“…Moreover, it is important to analyse different machine learning algorithms (e.g., Support Vector Machine [35]) and their potential limitations, such as overfitting or bias.…”

Section: Analysis Of Results For Blockiness and Blurrinessmentioning

confidence: 99%

Objective Quality Metrics Assessment for Cloud Gaming

Husić,

Kozić,

Baraković

2023

B&H Electrical Engineering

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This paper aims to provide objective quality metrics assessment for cloud gaming using machine learning algorithms. Three classification algorithms (i.e., Random Forest, Random Three and J-48) have been used for the development of models for objective quality assessment of two metrics: blurriness and blockiness. The results indicate that Random Forest has the best performance in this experimental case of objective quality metrics assessment for cloud gaming. Future research activities will cover comparison of a broad range of objective quality metrics and machine learning algorithms while using larger dataset to enhance the results significance.

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“…Moreover, it is important to analyse different machine learning algorithms (e.g., Support Vector Machine [35]) and their potential limitations, such as overfitting or bias.…”

Section: Analysis Of Results For Blockiness and Blurrinessmentioning

confidence: 99%

Objective Quality Metrics Assessment for Cloud Gaming

Husić,

Kozić,

Baraković

2023

B&H Electrical Engineering

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“…For the QoE prediction in HTTP video streaming, objectivity-aware and psychology-aware impacting parameters are considered [139], and the influence of buffering and initial delay is examined [140]. Moreover, in [139], the characteristics of video content, encoding settings, network transmission metrics, and playout buffer parameters are taken into account, while in [140], the proposed model demonstrates that buffering pattern descriptors, particularly those associated with the occurrence of the last stalling event, have a clear effect on QoE. Both the approaches use subjective metrics to assess QoE and are based on SL algorithms.…”

Section: A Video Streamingmentioning

confidence: 99%

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

et al. 2022

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The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-highdefinition video, and extended reality, requires the enhancement of end-users' perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user's personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.

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“…The latter is a representation of a set of QoE models with (usually) several ML features as input parameters, each valid over a subset of the feature space, where the subsets are defined by a decision tree. In earlier works [3], [7], M5P has shown its ability to identify a set of well-explainable QoE models. As our primary goal is to visualise and explain, over-fitting is not an issue, and thus, we train and test on the full data set.…”

Section: Algorithms and Evaluationmentioning

confidence: 99%

Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning

Ickin¹,

Fiedler²,

Vandikas³

2020

Preprint

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The development of QoE models by means of Machine Learning (ML) is challenging, amongst others due to small-size datasets, lack of diversity in user profiles in the source domain, and too much diversity in the target domains of QoE models. Furthermore, datasets can be hard to share between research entities, as the machine learning models and the collected user data from the user studies may be IPR-or GDPRsensitive. This makes a decentralized learning-based framework appealing for sharing and aggregating learned knowledge inbetween the local models that map the obtained metrics to the user QoE, such as Mean Opinion Scores (MOS). In this paper, we present a transfer learning-based ML model training approach, which allows decentralized local models to share generic indicators on MOS to learn a generic base model, and then customize the generic base model further using additional features that are unique to those specific localized (and potentially sensitive) QoE nodes. We show that the proposed approach is agnostic to specific ML algorithms, stacked upon each other, as it does not necessitate the collaborating localized nodes to run the same ML algorithm. Our reproducible results reveal the advantages of stacking various generic and specific models with corresponding weight factors. Moreover, we identify the optimal combination of algorithms and weight factors for the corresponding localized QoE nodes.

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Improving QoE prediction in mobile video through machine learning

Cited by 19 publications

References 20 publications

Objective Quality Metrics Assessment for Cloud Gaming

Objective Quality Metrics Assessment for Cloud Gaming

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning

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