Quality of Experience Management for YouTube: Clouds, FoG and the AquareYoum

Abstract

Over the last decade, Quality of Experience (QoE) has become a new, central paradigm for understanding the quality of networks and services. In particular, the concept has attracted the interest of communication network and service providers, since being able to guarantee good QoE to customers provides an opportunity for differentiation. In this paper we investigate the potential as well as the implementation challenges of QoE management in the Internet. Using YouTube video streaming service as example, we discuss the different elements that are required for the realization of the paradigm-shift towards truly user-centric network orchestration.To this end, we elaborate QoE management requirements for two complementary network scenarios (wireless mesh Internet access networks vs. global Internet delivery) and provide a QoE model for YouTube taking into account impairments like stalling and initial delay. We present two YouTube QoE monitoring approaches operating on the network and the end user level. Finally, we demonstrate how QoE can be dynamically optimized in both network scenarios with two exemplary concepts, AquareYoum and FoG, respectively. Our results show how QoE management can truly improve the user experience while at the same time increase the efficiency of network resource allocation.

Survey and Challenges of QoE Management Issues in Wireless Networks

“…While the authors argue that this approach ensures fair interpretation of subjectively perceived quality towards any end user or service, and operational efficiency in terms of no end-user involvement (guaranteeing universality of the QoE estimation), the notion of user singularity may be considered neglected. Hoßfeld et al [96] compare two YouTube QoE monitoring approaches operating at the end user level (described in the following heading) and within the network. A novel YouTube in-network (YiN) monitoring tool is proposed as a passive network monitoring tool.…”

“…Having surveyed several chosen QoE measurement approaches, we have classified them into ones that aim to perform QoE monitoring by acquiring data only at the client side [35,74,[88][89][90], only within the network [28,38,59,91,96], or by collecting data at both, the client side and within the network [97,98]. As stated previously, in order to assure accurate QoE estimation and identification of the causes of QoE degradation, measurements collected along the end-to-end service delivery path are needed.…”

“…While the collection of subjective assessments is generally conducted in the scope of empirical QoE studies targeted towards building accurate QoE models, objective measurements provide input for QoE prediction mechanisms and are commonly employed for QoE optimization and control purposes. Furthermore, in terms of the measurements environments that have been discussed, several approaches have been illustrated in a laboratory testbed [28,38,91,96,97], while others were demonstrated in real-life [35,59,74,90] or semi-real-life environments [88,89,98].…”

Analysis of Quality of Experience Evaluation Framework for RPL Protocol in Mobile IoT Environments under Manhattan Grid Mobility

“…QoE management for multimedia cloud services, particularly YouTube videos, relies on QoE models, monitoring, and optimization [18]. Initial delay and stalling effects are analyzed, and QoE monitoring approaches are explored.…”

Peripheral Sensing: Monitoring Quality of Experience for Video Services Based on Mobile Terminals

“…Another challenge for QoE estimation results from its multidisciplinary attributes [15]. In addition to technological factors, various non-technological factors, such as user-specific factors (preference, psychological and behavioral states) and context factors (location, time and environmental conditions), need to be taken into consideration and we call them external factors in this paper.…”