360° mulsemedia experience over next generation wireless networks - a reinforcement learning approach

The next generation of wireless networks targets aspiring key performance indicators, like very low latency, higher data rates and more capacity, paving the way for new generations of video streaming technologies, such as 360° or omnidirectional videos. One possible application that could revolutionize the streaming technology is the 360° MULtiple SEnsorial MEDIA (MULSEMEDIA) which enriches the 360° video content with other media objects like olfactory, haptic or even thermoceptic ones. However, the adoption of the 360° Mulsemedia applications might be hindered by the strict Quality of Service (QoS) requirements, like very large bandwidth and low latency for fast responsiveness to the users, inputs that could impact their Quality of Experience (QoE). To this extent, this paper introduces the new concept of 360° Mulsemedia as well as it proposes the use of Reinforcement Learning to enable QoS provisioning over the next generation wireless networks that influences the QoE of the end-users

An innovative machine learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360◦) video streaming. The proposed solution is deployed in a highly dy-namic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: Static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate

5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions

Structure Determination of Disordered Metallic Sub-Monolayers by Helium Scattering: A Theoretical and Experimental Study

An approach based on He scattering is used to develop an atomic-level structural model for an epitaxially grown disordered sub-monolayer of Ag on Pt(111) at 38K. Quantum scattering calculations are used to fit structural models to the measured angular intensity distribution of He atoms scattered from this system. The structure obtained corresponds to narrowly size-dispersed compact clusters with modest translational disorder, and not to fractals which might be expected due to the low surface temperature. The clusters have up to two layers in height, the lower one having few defects only. The relations between specific features of the angular scattering distribution, and properties such as the cluster sizes and shapes, the inter-cluster distance distribution etc., are discussed. The results demonstrate the usefulness of He scattering as a tool for unraveling new complex surface phases.Comment: 5 pages, 3 figures, to appear in Surf. Sci. Lett. Related papers available at http://neon.cchem.berkeley.edu/~dani/He-papers.htm

Conventional and manipulated growth of Cu-Cu(111)

Molecular beam epitaxy of Cu on Cu(111) was studied using thermal energy He scattering, in the temperature range between 100 and 450 K. Three-dimensional growth was observed in the whole temperature range. To determine the onset of various diffusion processes, submonolayer films formed by deposition at low temperature were annealed. Annealing proceeds in two steps. The first step is interpreted as a change in island shape, the second as Ostwald-ripening. A comparison with homoepitaxy on Pt(111) and Ag(111) is made. Growth manipulation was carried out by artificially increasing the island number density via intervention in the nucleation stage of each layer. The procedures applied were temperature reduction during nucleation as well as pulsed ion bombardment. These techniques enabled the convenient growth of good quality films consisting of a large number of monolayers. Finally, the use of oxygen as a surfactant modifying the growth mode was investigated. Under some growth conditions, pre-exposure of the surface to oxygen was found to induce weak He-intensity oscillations during deposition. The quality of the films grown in this way was, however, low

The influence of human factors on 360∘ mulsemedia QoE

Quality of Experience (QoE) is indelibly linked to the human side of the multimedia experience. Surprisingly, however, there is a paucity of research which explores the impact that human factors has in determining QoE. Whilst this is true of multimedia, it is even more starkly so as far as mulsemedia - applications that involve media engaging three or more of human senses - is concerned. Hence, in the study reported in this paper, we focus on an exciting subset of mulsemedia applications - 360∘ mulsemedia - particularly important given that the upcoming 5G technology is foreseen to be a key enabler for the proliferation of immersive Virtual Reality (VR) applications. Accordingly, we study the impact that human factors such as gender, age, prior computing experience, and smell sensitivity have on 360∘ mulsemedia QoE. Results showed insight into the potential of 360∘ mulsemedia to inspire and to enrich experiences for Generation Z - a generation empowered by rapidly advancing technology. Patterns of prior media usage and smell sensitivity play also an important role in influencing the QoE evaluation - users who have a preference for dynamic videos enjoy and find realistic the 360∘ mulsemedia experiences

Do I smell coffee? The tale of a 360º Mulsemedia experience

One of the main challenges in current multimedia networking environments is to find solutions to help accommodate the next generation of mobile application classes with stringent Quality of Service (QoS) requirements whilst enabling Quality of Experience (QoE) provisioning for users. One such application class, featured in this paper, is 360º mulsemedia—multiple sensorial media—which enriches 360º video by adding sensory effects that stimulate human senses beyond those of sight and hearing, such as the tactile and olfactory ones. In this paper, we present a conceptual framework for 360º mulsemedia delivery and a 360º mulsemedia-based prototype that enables users to experience 360º mulsemedia content. User evaluations revealed that higher video resolutions do not necessarily lead to the highest QoE levels in our experimental setup. Therefore, bandwidth savings can be leveraged with no detrimental impact on QoE

360° Mulsemedia: a way to improve subjective QoE in 360° videos

Previous research has shown that adding multisensory media-mulsemedia-to traditional audiovisual content has a positive effect on user Quality of Experience (QoE). However, the QoE impact of employing mulsemedia in 360° videos has remained unexplored. Accordingly, in this paper, a QoE study for watching a 360° video-with and without multisensory effects-in a full free-viewpoint VR setting is presented. The parametric space we considered to influence the QoE consists of the encoding quality and the motion level of the transmitted media. To achieve our research aim, we propose a wearable VR system that provides multisensory enhancement of 360° videos. Then, we utilise its capabilities to systematically evaluate the effects of multisensory stimulation on perceived quality degradation for videos with different motion levels and encoding qualities. Our results make a strong case for the inclusion of multisensory effects in 360° videos, as they reveal that both user-perceived quality, as well as enjoyment, are significantly higher when mulsemedia (as opposed to traditional multimedia) is employed in this context. Moreover, these observations hold true independent of the underlying 360° video encoding quality-thus QoE can be significantly enhanced with a minimal impact on networking resources

A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers

Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings

Towards 5G: A reinforcement learning-based scheduling solution for data traffic management

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements