1,241 research outputs found
An Edge and Fog Computing Platform for Effective Deployment of 360 Video Applications
This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and
services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaImmersive video applications based on 360 video
streaming require high-bandwidth, high-reliability and lowlatency
5G connectivity but also flexible, low-latency and costeffective
computing deployment. This paper proposes a novel
solution for decomposing and distributing the end-to-end 360
video streaming service across three computing tiers, namely
cloud, edge and constrained fog, in order of proximity to the
end user client. The streaming service is aided with an adaptive
viewport technique. The proposed solution is based on the H2020
5G-CORAL system architecture using micro-services-based design
and a unified orchestration and control across all three tiers
based on Fog05. Performance evaluation of the proposed solution
shows noticeable reduction in bandwidth consumption, energy
consumption, and deployment costs, as compared to a solution
where the streaming service is all delivered out of one computing
location such as the Cloud.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586)
Immersive interconnected virtual and augmented reality : a 5G and IoT perspective
Despite remarkable advances, current augmented and virtual reality (AR/VR) applications are a largely individual and local experience. Interconnected AR/VR, where participants can virtually interact across vast distances, remains a distant dream. The great barrier that stands between current technology and such applications is the stringent end-to-end latency requirement, which should not exceed 20 ms in order to avoid motion sickness and other discomforts. Bringing AR/VR to the next level to enable immersive interconnected AR/VR will require significant advances towards 5G ultra-reliable low-latency communication (URLLC) and a Tactile Internet of Things (IoT). In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality
Real-Time Neural Video Recovery and Enhancement on Mobile Devices
As mobile devices become increasingly popular for video streaming, it's
crucial to optimize the streaming experience for these devices. Although deep
learning-based video enhancement techniques are gaining attention, most of them
cannot support real-time enhancement on mobile devices. Additionally, many of
these techniques are focused solely on super-resolution and cannot handle
partial or complete loss or corruption of video frames, which is common on the
Internet and wireless networks.
To overcome these challenges, we present a novel approach in this paper. Our
approach consists of (i) a novel video frame recovery scheme, (ii) a new
super-resolution algorithm, and (iii) a receiver enhancement-aware video bit
rate adaptation algorithm. We have implemented our approach on an iPhone 12,
and it can support 30 frames per second (FPS). We have evaluated our approach
in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows
that our approach enables real-time enhancement and results in a significant
increase in video QoE (Quality of Experience) of 24\% - 82\% in our video
streaming system
Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise
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