A network paradigm for very high capacity mobile and fixed telecommunications ecosystem sustainable evolution

For very high capacity networks (VHC), the main objective is to improve the quality of the end-user experience. This implies compliance with key performance indicators (KPIs) required by applications. Key performance indicators at the application level are throughput, download time, round trip time, and video delay. They depend on the end-to-end connection between the server and the end-user device. For VHC networks, Telco operators must provide the required application quality. Moreover, they must meet the objectives of economic sustainability. Today, Telco operators rarely achieve the above objectives, mainly due to the push to increase the bit-rate of access networks without considering the end-to-end KPIs of the applications. The main contribution of this paper concerns the definition of a deployment framework to address performance and cost issues for VHC networks. We show three actions on which it is necessary to focus. First, limiting bit-rate through video compression. Second, contain the rate of packet loss through artificial intelligence algorithms for line stabilization. Third, reduce latency (i.e., round-trip time) with edge-cloud computing. The concerted and gradual application of these measures can allow a Telco to get out of the ultra-broadband "trap" of the access network, as defined in the paper. We propose to work on end-to-end optimization of the bandwidth utilization ratio. This leads to a better performance experienced by the end-user. It also allows a Telco operator to create new business models and obtain new revenue streams at a sustainable cost. To give a clear example, we describe how to realize mobile virtual and augmented reality, which is one of the most challenging future services.Comment: 42 pages, 4 tables, 6 figures. v2: Revised Englis

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

Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming

Whereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and levels of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps

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

A meter band rate mechanism to improve the native QoS capability of OpenFlow and OpenDaylight

The exponential growth of mobile connected devices with advanced multimedia features imposes a requirement to enhance quality of service (QoS) from heterogeneous systems and networks. In order to satisfy mission-critical multimedia QoS requirements new generation mobile networks must present content-optimized mechanisms in order to use valuable network resources efficiently and provide QoS requirements for each application. This research explores a novel solution for quality of service performance for streaming mission-critical video data in OpenFlow SDN networks. A Meter Band Rate Evaluator (MBE) Mechanism is proposed based on a new band rate description language to improve the native QoS capability of OpenFlow and OpenDaylight. Its design and development are presented and the mechanism is verified through a simulated experiment in an SDN testbed. The results revealed a significant percentage increase in QoS performance when the MBE was enabled. These findings provide support and validation for the effectiveness of the MBE to enhance the native capability of OpenFlow and OpenDaylight for efficient QoS provision