Economics of Quality of Experience

International audienc

Biological Information Based QoE Management in HTTP Adaptive Streaming Services

芝浦工業大学2018年

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANETs) are a new emerging technology which has attracted enormous interest over the last few years. It enables vehicles to communicate with each other and with roadside infrastructures for many applications. One of the promising applications is multimedia services for traffic safety or infotainment. The video service requires a good quality to satisfy the end-user known as the Quality of Experience (QoE). Several models have been suggested in the literature to measure or predict this metric. In this paper, we present an overview of interesting researches, which propose QoE models for video streaming over VANETs. The limits and deficiencies of these models are identified, which shed light on the challenges and real problems to overcome in the future

Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users' QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme

A smartphone agent for QoE evaluation and user classification over mobile networks

The continuous growth of mobile users and bandwidth-consuming applications and the shortage of radio resources put a serious challenge on how to efficiently exploit existing networks and contemporary improve Quality of Experience. One of the most relevant problem for network operators is thus to find an explicit relationship between QoS and QoE, for the purpose of maximizing the latter while saving precious resources. In order to accomplish this challenging task, we present TeleAbarth, an innovative Android application entirely developed at TelecomItalia Laboratories, able to contemporary collect network measurements and end-users quality feedback regarding the use of smartphone applications. We deployed TeleAbarth in a field experimentation in order to study the relationship between QoS and QoE for video streaming applications, in terms of downstream bandwidth and video loading time. On the basis of the results obtained, we propose a technique to classify user behavior through his or her reliability, sensibility and fairness

A Survey on Modeling of Human States in Communication Behavior

The Technical Committee on Communication BehaviorEngineering addresses the research question How do we construct a com-munication network system that includes users?. The growth in highlyfunctional networks and terminals has brought about greater diversity inusers\u27 lifestyles and freed people from the restrictions of time and place.Under this situation, the similarities of human behavior cause traffic aggre-gation and generate new problems in terms of the stabilization of networkservice quality. This paper summarizes previous studies relevant to com-munication behavior from a multidisciplinary perspective and discusses theresearch approach adopted by the Technical Committee on CommunicationBehavior Engineering

MWQoE: A user-centered context-aware model for evaluating the Mobile Web Quality of Experience

This article presents a human-system interaction modelling approach for quantifying Web Quality of Experience (Web QoE) for mobile devices. It builds on current QoE and Web QoE research, and by fusing together data that is available on modern mobile devices, constructs a novel Mobile Web QoE (MWQoE) model that is user-centered and context-aware. The MWQoE model uses Bayesian Networks and works under uncertainty, while the MWQoE metric uses Utility Theory and delivers a quantified characterization of MWQoE on a single scale in specific scenarios. The importance of MWQoE lies in the fact that online content and service providers need insights into their users in order to understand how the experience in using their products is perceived

Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to quantify the perceptual QoE of the streaming video users and monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Towards this end, we present LSTM-QoE, a recurrent neural network based QoE prediction model using a Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time varying QoE. Based on an evaluation over several publicly available continuous QoE databases, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides superior performance across these databases. Further, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for QoE prediction

A QoE based performance study of mobile peer-to-peer live video streaming

Peer-to-peer (P2P) Mobile Ad Hoc Networks (MANETs) are widely envisioned to be a practical platform to mobile live video streaming applications (e.g., mobile IPTV). However, the performance of such a streaming solution is still largely unknown. As such, in this paper, we aim to quantify the streaming performance using a Quality of Experience (QoE) based approach. Our simulation results indicate that video streaming performance is highly sensitive to the video chunk size. Specifically, if the chunk size is small, performance, in terms of both QoE and QoS, is guaranteed but at the expense of a higher overhead. On the other hand, if chunk size is increased, performance can degrade quite rapidly. Thus, it needs some careful fine tuning of chunk size to obtain satisfactory QoE performance. © 2012 IEEE.published_or_final_versio