A Survey on QoE-oriented Wireless Resources Scheduling <s> E. Discussion <s> We present an approach for the recognition of acted emotional states based on the analysis of body movement and gesture expressivity. According to research showing that distinct emotions are often associated with different qualities of body movement, we use non- propositional movement qualities (e.g. amplitude, speed and fluidity of movement) to infer emotions, rather than trying to recognise different gesture shapes expressing specific emotions. We propose a method for the analysis of emotional behaviour based on both direct classification of time series and a model that provides indicators describing the dynamics of expressive motion cues. Finally we show and interpret the recognition rates for both proposals using different classification algorithms. <s> BIB001 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> E. Discussion <s> Any architectural optimization aims at satisfying the end user. However, modern architectures execute with little to no knowledge about the individual user. If architectures could determine whether their users are satisfied, they could provide higher efficiency; improved reliability, reduced power consumption, increased security, and a better user experience. A major reason for this limitation is their input devices. Specifically, the traditional input devices (e.g., the mouse and keyboard) provide limited information about the user. In this paper, we make a case for the addition of new biometric input devices for providing the computer information about the userpsilas physiological traits. We explore three biometric devices as potential sensors: an eye tracker, a galvanic skin response (GSR) sensor, and force sensors. We first present two user studies that explore the link between the sensor readings and user satisfaction when the performance of the processor is varied as a video game is being played. In the first study, we drastically drop the processor clock frequency at a set point in the game. In the second study, we set the clock frequency to randomly-selected levels during game play. Both studies show that there are significant changes in human physiological traits as performance decreases. More importantly, we show that physiological changes correlate strongly to the satisfaction levels reported by the users. Based upon these observations, we construct a Physiological Traits-based Power-management (PTP) system that can be applied to existing dynamic voltage and frequency scaling (DVFS) schemes. We apply PTP to a typical CPU-utilization-based adaptive DVFS policy and evaluate our scheme using a third user study. An aggressive version of our PTP scheme reduces the total system power consumption of a laptop by up to 33.3% for an application averaged across users (18.1% averaged across three applications), while a conservative version reduces the total system power consumption by up to 25.6% across users (11.4% averaged across three applications). <s> BIB002 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> E. Discussion <s> This project explores the idea of facial expression for automated feedback in teaching. We show how automatic real-time facial expression recognition can be effectively used to estimate the difficulty level, as perceived by an individual student, of a delivered lecture. We also show that facial expression is predictive of an individual student's preferred rate of curriculum presentation at each moment in time. On a video lecture viewing task, training on less than two minutes of recorded facial expression data and testing on a separate validation set, our system predicted the subjects' self-reported difficulty scores with mean accuracy of 0.42 (Pearson r) and their preferred viewing speeds with mean accuracy of 0.29. Our techniques are fully automatic and have potential applications for both intelligent tutoring systems (ITS) and standard classroom environments. <s> BIB003 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> E. Discussion <s> Thanks to its in-network drop-based adaptation capabilities, H.264 Scalable Video Coding is perceived as an effective approach for delivering video over networks characterized by sudden large bandwidth fluctuations, such as Wireless LANs. Performance may be boosted by the adoption of application-aware/cross-layer schedulers devised to intelligently drop video data units (NALUs), so that i) decoding dependencies are preserved, and ii) the quality perceived by the end users is maximized. In this paper, we provide a theoretical formulation of a QoE utility-optimal cross-layer scheduling problem for H.264 SVC downlink delivery over WLANs. We show that, because of the unique characteristics of the WLAN MAC operation, this problem significantly differs from related approaches proposed for scheduled wireless technologies, especially when the WLAN carries background traffic in the uplink direction. From these theoretical insights, we derive, design, implement and experimentally assess a simple practical scheduling algorithm, whose performance is very close to the optimal solution. <s> BIB004 </s> A Survey on QoE-oriented Wireless Resources Scheduling <s> E. Discussion <s> The ever increasing demand for high bandwidth, low latency multimedia applications on mobile devices is set to pose a considerable challenge on the bandwidth allocation and multiplexing mechanisms in LTE and future wireless networks. This paper proposes a low overhead Scalable Video Coding (SVC) based dynamic adaptive streaming framework (called TLS-AV) which attempt to maintain a minimum satisfactory Quality of Experience (QoE) for all end users even during transient network overloads. Two fast and efficient radio resource allocation heuristics namely, the TLS-AV Water-Filling Heuristic (TWH) and TLS-AV Balanced Water-Filling Heuristic (TBWH) have been implemented over the proposed framework. Experimental results reveal that both the developed heuristics are able to restrict packet loss rate below ∼ 10% while simultaneously achieving high video qualities and fairness among the transmitted qualities of video flows, on average. <s> BIB005
All the scheduling methods referred above perform resource allocation and/or prioritization at the wireless MAC/physical layer level, which is the scope of this work. Nevertheless, there are also QoE-aware algorithms that permit to retain a fully standard and application-layer unaware MAC/physical operation while still taking into account the wireless channel specificities (e.g., BIB004 - BIB005 ). Taking into account the scheduling strategies previously mentioned, video streaming is clearly the application that can benefit more from QoE-aware schedulers. In addition, this will keep being an important topic of research, since not only video streaming is now the most popular application on the Internet, having accounted for 55% of total mobile data traffic in 2015, but also three-fourths of the world's mobile data traffic will be video by 2020  . It is also foreseen that in the future, with the help of various biometric sensors embedded in smartphones (some of them already available nowadays), QoE assessment will be performed directly from live human biometric data such as galvanic skin response and pupil dynamics BIB002 , body gestures BIB001 , and facial expressions BIB003 . This approach will enhance the wireless systems capability of gathering more accurate QoE data for better serving a user at any instance.