Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from any-where at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues,we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR device

A survey on haptic technologies for mobile augmented reality

Augmented Reality (AR) and Mobile Augmented Reality (MAR) applications have gained much research and industry attention these days. The mobile nature of MAR applications limits users' interaction capabilities such as inputs, and haptic feedbacks. This survey reviews current research issues in the area of human computer interaction for MAR and haptic devices. The survey first presents human sensing capabilities and their applicability in AR applications. We classify haptic devices into two groups according to the triggered sense: cutaneous/tactile: touch, active surfaces, and mid-air, kinesthetic: manipulandum, grasp, and exoskeleton. Due to the mobile capabilities of MAR applications, we mainly focus our study on wearable haptic devices for each category and their AR possibilities. To conclude, we discuss the future paths that haptic feedbacks should follow for MAR applications and their challenges

CloudAR: A Cloud-based Framework for Mobile Augmented Reality

Computation capabilities of recent mobile devices enable natural feature processing for Augmented Reality (AR). However, mobile AR applications are still faced with scalability and performance challenges. In this paper, we propose CloudAR, a mobile AR framework utilizing the advantages of cloud and edge computing through recognition task offloading. We explore the design space of cloud-based AR exhaustively and optimize the offloading pipeline to minimize the time and energy consumption. We design an innovative tracking system for mobile devices which provides lightweight tracking in 6 degree of freedom (6DoF) and hides the offloading latency from users' perception. We also design a multi-object image retrieval pipeline that executes fast and accurate image recognition tasks on servers. In our evaluations, the mobile AR application built with the CloudAR framework runs at 30 frames per second (FPS) on average with precise tracking of only 1~2 pixel errors and image recognition of at least 97% accuracy. Our results also show that CloudAR outperforms one of the leading commercial AR framework in several performance metrics

Guadalupe: a browser design for heterogeneous hardware

Mobile systems are embracing heterogeneous architectures by getting more types of cores and more specialized cores, which allows applications to be faster and more efficient. We aim at exploiting the hardware heterogeneity from the browser without requiring any changes to either the OS or the web applications. Our design, Guadalupe, can use hardware processing units with different degrees of capability for matched browser services. It starts with a weak hardware unit, determines if and when a strong unit is needed, and seamlessly migrates to the strong one when necessary. Guadalupe not only makes more computing resources available to mobile web browsing but also improves its energy proportionality. Based on Chrome for Android and TI OMAP4, We provide a prototype browser implementation for resource loading and rendering. Compared to Chrome for Android, we show that Guadalupe browser for rendering can increase other 3D application's frame rate by up to 767% and save 4.7% of the entire system's energy consumption. More importantly, by using the two cases, we demonstrate that Guadalupe creates the great opportunity for many browser services to get better resource utilization and energy proportionality by exploiting hardware heterogeneity

DRIVESHAFT: Improving Perceived Mobile Web Performance

With mobiles overtaking desktops as the primary vehicle of Internet consumption, mobile web performance has become a crucial factor for websites as it directly impacts their revenue. In principle, improving web performance entails squeezing out every millisecond of the webpage delivery, loading, and rendering. However, on a practical note, an illusion of faster websites suffices. This paper presents DriveShaft, a system envisioned to be deployed in Content Delivery Networks, which improves the perceived web performance on mobile devices by reducing the time taken to show visually complete web pages, without requiring any changes in websites, browsers, or any actions from end-user. DriveShaft employs (i) crowdsourcing, (ii) on-the-fly JavaScript injection, (iii) privacy preserving desensitization, and (iv) automatic HTML generation to achieve its goals. Experimental evaluations using 200 representative websites on different networks (Wi-Fi and 4G), different devices (high-end and low-end phones) and different browsers, show a reduction of 5x in the time required to see a visually complete website while giving a perception of 5x-6x faster page loading.Comment: 13 pages, 14 figure

Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks (C-RANs)

Over the last few years, Cloud Radio Access Network (C-RAN) has arisen as a transformative architecture for 5G cellular networks that brings the flexibility and agility of cloud computing to wireless communications. At the same time, content caching in wireless networks has become an essential solution to lower the content-access latency and backhaul traffic loading, which translate into user Quality of Experience (QoE) improvement and network cost reduction. In this article, a novel Cooperative Hierarchical Caching (CHC) framework in C-RAN is introduced where contents are jointly cached at the BaseBand Unit (BBU) and at the Radio Remote Heads (RRHs). Unlike in traditional approaches, the cache at the BBU, cloud cache, presents a new layer in the cache hierarchy, bridging the latency/capacity gap between the traditional edge-based and core-based caching schemes. Trace-driven simulations reveal that CHC yields up to 80% improvement in cache hit ratio, 21% decrease in average content-access latency, and 20% reduction in backhaul traffic load compared to the edge-only caching scheme with the same total cache capacity. Before closing the article, several challenges and promising opportunities for deploying content caching in C-RAN are highlighted towards a content-centric mobile wireless network.Comment: to appear on IEEE Network, July 201

Designing a Network Based System for Delivery of Remote Mine Services

There is a great body of work in the areas of tele-assistance/tele-collaboration offering novel and effective ways to improve collaboration between personnel located at a remote mine site and off-site personnel located in major metropolitan areas. Much of this work involves the use of high-bandwidth communications or targeted sensory experiences using large format displays. There are also existing remote access technologies but these suffer from limited functionality (providing text, voice, video or one-way desktop sharing), are often poorly supported in the security-conscious corporate environment and require complicated set up processes. There is currently no singular piece of remote collaboration technology that is suitable for the delivery of high-quality planning and scheduling services to clients at a mining site from a remote operating centre. In response to this issue, as part of a research and technology development effort between CSIRO and a mining engineering firm, we have developed a concept of remote mining engineer (RME) and conducted a functional requirements analysis for delivering mining engineering services to mine sites remotely. Based on the obtained requirements, a further study was performed to characterise existing technologies and to identify the scope for future work in designing and prototyping a network based system for RME. We report on the method and findings of this study in this paper

A Collaborative Untethered Virtual Reality Environment for Interactive Social Network Visualization

The increasing prevalence of Virtual Reality technologies as a platform for gaming and video playback warrants research into how to best apply the current state of the art to challenges in data visualization. Many current VR systems are noncollaborative, while data analysis and visualization is often a multi-person process. Our goal in this paper is to address the technical and user experience challenges that arise when creating VR environments for collaborative data visualization. We focus on the integration of multiple tracking systems and the new interaction paradigms that this integration can enable, along with visual design considerations that apply specifically to collaborative network visualization in virtual reality. We demonstrate a system for collaborative interaction with large 3D layouts of Twitter friend/follow networks. The system is built by combining a 'Holojam' architecture (multiple GearVR Headsets within an OptiTrack motion capture stage) and Perception Neuron motion suits, to offer an untethered, full-room multi-person visualization experience

Artificial Intelligence-Defined 5G Radio Access Networks

Massive multiple-input multiple-output antenna systems, millimeter wave communications, and ultra-dense networks have been widely perceived as the three key enablers that facilitate the development and deployment of 5G systems. This article discusses the intelligent agent in 5G base station which combines sensing, learning, understanding and optimizing to facilitate these enablers. We present a flexible, rapidly deployable, and cross-layer artificial intelligence (AI)-based framework to enable the imminent and future demands on 5G and beyond infrastructure. We present example AI-enabled 5G use cases that accommodate important 5G-specific capabilities and discuss the value of AI for enabling beyond 5G network evolution