Adequate Quality Of Experience Research Articles

QoE‐aware traffic monitoring based on user behavior in video streaming services

SummaryIn recent years, online video content has gained a lot of popularity and the exponential growth of video traffic continues in every area of the connected world. Thus, understanding the quality of experience (QoE) perceived by end‐users of video streaming services is important for both network operators and over the top providers since estimating end user's QoE has become one of the main points to meet user expectations. However, it is not trivial to ensure an adequate QoE since user experience is affected by various influencing factors (e.g., context factors, human factors, and system factors), and it is still challenging to identify the QoE key influencing factors. To address these challenges, we focused on improving QoE estimation and management strategies by exploiting valuable human and context information because the influence of social contextual information and user behavior on the perceptual quality is often neglected. In this article, we first proposed a classification of influence factors into four categories which are: system factors, human factors, context factors, and social‐behavioral factors. We developed a monitoring web application where video content is played to end‐users so that subjective and objective video metrics are collected. We built a new machine learning (ML) based model for QoE prediction. We used well‐known supervised ML algorithms like decision tree, k‐nearest neighbors, and support vector machine. Finally, we proposed a QoE management approach in the context of software defined network/multi‐access edge computing that implements the proposed QoE prediction model to optimize the video delivery transmission chain.

Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE

In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.

A QoE-Driven Traffic Steering Algorithm for LTE Networks

Due to the diversity of mobile services and raising user expectations, mobile network management has changed its focus from Quality of Service (QoS) to Quality of Experience (QoE). As a consequence, classical network optimization procedures must be updated accordingly. One of these procedures is traffic sharing, whose aim is to redistribute traffic among adjacent cells so as to provide an adequate QoE to subscribers. In this work, a novel QoE-driven traffic sharing algorithm based on mobility load balancing is proposed for LTE networks offering services of very different nature. Unlike previous approaches, where the aim was to balance some QoS indicator, the aim here is to equalize the QoE provided by all cells in the network. For this purpose, the handover margins between adjacent cells are tuned on a per-adjacency or per-service basis based on QoE measurements collected in the network management system. Method assessment is based on a dynamic system-level simulator implementing a realistic LTE scenario. Results show that the proposed QoE-driven traffic sharing algorithm alleviates QoE problems by equalizing user QoE throughout the scenario.

Experimental QoE Evaluation of Multicast Video Delivery over IEEE 802.11aa WLANs

The IEEE 802.11aa amendment standardised the Group Addressed Transmission Service (GATS), which extends 802.11 WLANs with a novel set of MAC mechanisms to support an effective and efficient multicast video service. The key challenge with GATS is the selection of the best scheme and its configuration for a given network scenario, as the standard does not provide any guidelines nor any assessment of the performance of each mechanism. Although some previous studies have addressed this challenge, their evaluation is either via analysis or simulations under non-realistic assumptions, or based on Quality of Service (QoS) metrics instead of video quality metrics, which are required for a proper video performance assessment. In this paper, we deploy a mid-size real-life testbed and develop a thoughtful methodology to perform an extensive Quality of Experience (QoE) evaluation of GATS under a variety of scenarios. We analyse the performance of the novel schemes under ideal conditions, as well as under controlled and non-controlled interference, assessing their ability to provide an adequate QoE and quantifying the resources left for other type of traffic. Ours is the first thorough QoE evaluation of GATS in a real-life scenario, providing key insights on their performance, and can be used to derive configuration guidelines for the schemes.

Mobility-Aware and Radio Characteristics Enabled Routing for Video Streaming over MANETs

Providing an adequate level of quality-of-experience (QoE) for multimedia applications in mobile ad-hoc networks (MANETs) is a challenging task due to its environmental characteristics, such as dynamic network topology, variable bit-rates of transmitting traffic, and fluctuating link conditions. In this paper, we propose a QoS provisioning mobility-aware and radio characteristics enabled routing for video streaming over MANETs (Q-RCNM) based routing protocol, and its multipath variant (i.e., Q-RCNMM). Our protocols ensure adequate QoE to the end users while receiving the multimedia traffic. Q-RCNM uses link-bandwidth, link-delay, and link-transmission efficiency ratio as QoS metrics along with the node mobility during its QoS-aware route discovery phase. We propose novel techniques for accurately estimating the aforementioned QoS metrics. Additionally, we design a joint analytical model that simultaneously considers the dynamic values of the aforementioned metrics to generate a single link-QoS value. Simulation results in terms of peak signal-to-noise ratio (PSNR), delay, route throughput, transmission efficiency, and route lifetime, shows the efficiency of our proposal when compared with similar state-of-the-art techniques.