Survey on QoE\QoS Correlation Models For Multimedia Services (1306.0221v1)

Abstract: This paper presents a brief review of some existing correlation models which attempt to map Quality of Service (QoS) to Quality of Experience (QoE) for multimedia services. The term QoS refers to deterministic network behaviour, so that data can be transported with a minimum of packet loss, delay and maximum bandwidth. QoE is a subjective measure that involves human dimensions; it ties together user perception, expectations, and experience of the application and network performance. The Holy Grail of subjective measurement is to predict it from the objective measurements; in other words predict QoE from a given set of QoS parameters or vice versa. Whilst there are many quality models for multimedia, most of them are only partial solutions to predicting QoE from a given QoS. This contribution analyses a number of previous attempts and optimisation techniquesthat can reliably compute the weighting coefficients for the QoS/QoE mapping.

• The paper presents a comprehensive survey reviewing QoE/QoS correlation models in multimedia services, emphasizing challenges in mapping objective network metrics to subjective user experience.
• It systematically examines methodologies including the IQX hypothesis, VQM-based mapping, and machine learning techniques to improve predictive accuracy.
• The study underscores the need for integrative models that account for diverse factors such as user behavior and network conditions to enhance multimedia service quality.

A Survey on QoE/QoS Correlation Models for Multimedia Services

The paper "Survey on QoE/QoS Correlation Models for Multimedia Services" by Mohammed Alreshoodi and John Woods provides a thorough examination of various models designed to correlate Quality of Service (QoS) with Quality of Experience (QoE) in multimedia services. QoS entails objective parameters like packet loss, delay, and bandwidth that determine network performance, whereas QoE encompasses user-centric, subjective evaluation metrics that reflect the user's perception and expectations of service quality. The inherent challenge of translating QoS metrics into meaningful QoE insights forms the core discussion in this paper.

Overview of QoS and QoE

The paper introduces the conceptual layering of QoS and QoE within the context of the OSI model. Generally, QoS metrics are scrutinized in the application and network layers, focusing on service parameters such as frame rate, resolution, delay, and jitter, which influence QoE. QoE is often perceived as an extension, a pseudo-layer encompassing user experience. The challenges in quantifying QoE arise due to the multi-dimensioned human factors involved, which include not just the objective network performance but also user expectations, context, and the type and manner of media consumption.

Methodologies in QoE Measurement

The paper delineates QoE measurement approaches mainly into subjective, objective, or a combination of both. Subjective methods include the Mean Opinion Score (MOS), whereas objective assessments often utilize techniques like Peak Signal to Noise Ratio (PSNR), which are sometimes extended to subjective predictions using mapping techniques. Objective methods are subdivided into different models like parametric packet-layer and bit-stream models, which leverage network parameters for predictive analytics. The complexity of mapping objective QoS indices to subjective QoE scores is aptly highlighted in terms of necessary linear and non-linear correlation functions.

Analytical Review of Correlation Models

Alreshoodi and Woods categorize the reviewed QoS/QoE correlation models and elaborate on several methodological approaches:

1. IQX Hypothesis: Proposes an exponential correlation between QoS and QoE, suggesting superior mapping efficiency over logarithmic models.
2. VQM-based Mapping: Utilizes multidimensional QoS parameters to predict Video Quality Metric (VQM) scores as proxy indicators for QoE.
3. Machine Learning Techniques: Applies algorithms like Decision Trees (DT) and Support Vector Machines (SVM) for improving QoE predictive accuracy.
4. Crowdsourcing and Hybrid Models: Incorporates user diversity via crowdsourcing while considering both objective metrics and adaptive learning models for a holistic analysis.

Implications and Future Perspectives

A notable insight from the paper is the under-explored interaction between the QoS variables and various influencing factors like user behavior, device characteristics, and environmental contexts. This underlines a call for innovative methodologies to precisely encapsulate the subjective nature of QoE while leveraging network metrics effectively.

The implications of these models extend extensively towards improved network management and multimedia service provisioning. The pursuit of robust QoE metrics could yield significant advancements in real-time adaptive systems, promising enhanced user satisfaction and optimized resource utilization.

Conclusion

The paper echoes a central theme: the multifaceted nature of QoE necessitates an integrative approach to quantifying its relationship with QoS. While existing models offer valuable insights, they often encapsulate only partial mappings due to the diverse and evolving landscape of multimedia consumption environments. As AI and machine learning advance, future research may develop more comprehensive models capable of a nuanced understanding of user experience beyond conventional QoE assessment frameworks, potentially transforming current paradigms in distributed and parallel multimedia service systems.