Online Reinforcement Learning for Dynamic Multimedia Systems

In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table

In-Network View Synthesis for Interactive Multiview Video Systems

To enable Interactive multiview video systems with a minimum view-switching delay, multiple camera views are sent to the users, which are used as reference images to synthesize additional virtual views via depth-image-based rendering. In practice, bandwidth constraints may however restrict the number of reference views sent to clients per time unit, which may in turn limit the quality of the synthesized viewpoints. We argue that the reference view selection should ideally be performed close to the users, and we study the problem of in-network reference view synthesis such that the navigation quality is maximized at the clients. We consider a distributed cloud network architecture where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. In order to satisfy last-hop bandwidth constraints from the cloudlet to the users, a cloudlet re-samples viewpoints of the 3D scene into a discrete set of views (combination of received camera views and virtual views synthesized) to be used as reference for the synthesis of additional virtual views at the client. This in-network synthesis leads to better viewpoint sampling given a bandwidth constraint compared to simple selection of camera views, but it may however carry a distortion penalty in the cloudlet-synthesized reference views. We therefore cast a new reference view selection problem where the best subset of views is defined as the one minimizing the distortion over a view navigation window defined by the user under some transmission bandwidth constraints. We show that the view selection problem is NP-hard, and propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem. Simulation results finally confirm the performance gain offered by virtual view synthesis in the network

MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications

According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente

Pervasive and standalone computing: The perceptual effects of variable multimedia quality.

The introduction of multimedia on pervasive and mobile communication devices raises a number of perceptual quality issues, however, limited work has been done examining the 3-way interaction between use of equipment, quality of perception and quality of service. Our work measures levels of informational transfer (objective) and user satisfaction (subjective)when users are presented with multimedia video clips at three different frame rates, using four different display devices, simulating variation in participant mobility. Our results will show that variation in frame-rate does not impact a user’s level of information assimilation, however, does impact a users’ perception of multimedia video ‘quality’. Additionally, increased visual immersion can be used to increase transfer of video information, but can negatively affect the users’ perception of ‘quality’. Finally, we illustrate the significant affect of clip-content on the transfer of video, audio and textual information, placing into doubt the use of purely objective quality definitions when considering multimedia presentations

TransparentHMD: Revealing the HMD User's Face to Bystanders

While the eyes are very important in human communication, once a user puts on a head mounted display (HMD), the face is obscured from the outside world's perspective. This leads to communication problems when bystanders approach or collaborate with an HMD user. We introduce transparentHMD, which employs a head-coupled perspective technique to produce an illusion of a transparent HMD to bystanders. We created a self contained system, based on a mobile device mounted on the HMD with the screen facing bystanders. By tracking the relative position of the bystander using the smartphone's camera, we render an adapting perspective view in realtime that creates the illusion of a transparent HMD. By revealing the user's face to bystanders, our easy to implement system allows for opportunities to investigate a plethora of research questions particularly related to collaborative VR systems