Gwendal Simon

Massimo Gallo, Alessandro Finamore, Gwendal Simon et al.

Live traffic analysis at the first aggregation point in the ISP network enables the implementation of complex traffic engineering policies but is limited by the scarce processing capabilities, especially for Deep Learning (DL) based analytics. The introduction of specialized hardware accelerators i.e., Tensor Processing Unit (TPU), offers the opportunity to enhance the processing capabilities of network devices at the edge. Yet, to date, no packet processing pipeline is capable of offering DL-based analysis capabilities in the data-plane, without interfering with network operations. In this paper, we present FENXI, a system to run complex analytics by leveraging TPU. The design of FENXI decouples forwarding operations and traffic analytics which operates at different granularities i.e., packet and flow levels. We conceive two independent modules that asynchronously communicate to exchange network data and analytics results, and design data structures to extract flow level statistics without impacting per-packet processing. We prototyped and evaluated FENXI on general-purpose servers considering both adversarial and realistic network conditions. Our analysis shows that FENXI can sustain 100 Gbps line rate traffic processing requiring only limited resources, while also dynamically adapting to variable network conditions.

Jacob Chakareski, Ridvan Aksu, Xavier Corbillon et al.

The growing popularity of virtual and augmented reality communications and 360° video streaming is moving video communication systems into much more dynamic and resource-limited operating settings. The enormous data volume of 360° videos requires an efficient use of network bandwidth to maintain the desired quality of experience for the end user. To this end, we propose a framework for viewport-driven rate-distortion optimized 360° video streaming that integrates the user view navigation pattern and the spatiotemporal rate-distortion characteristics of the 360° video content to maximize the delivered user quality of experience for the given network/system resources. The framework comprises a methodology for constructing dynamic heat maps that capture the likelihood of navigating different spatial segments of a 360° video over time by the user, an analysis and characterization of its spatiotemporal rate-distortion characteristics that leverage preprocessed spatial tilling of the 360° view sphere, and an optimization problem formulation that characterizes the delivered user quality of experience given the user navigation patterns, 360° video encoding decisions, and the available system/network resources. Our experimental results demonstrate the advantages of our framework over the conventional approach of streaming a monolithic uniformly encoded 360° video and a state-of-the-art reference method. Considerable video quality gains of 4 - 5 dB are demonstrated in the case of two popular 4K 360° videos.

Xavier Corbillon, Gwendal Simon, Alisa Devlic et al.

The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.

Laura Toni, Ramon Aparicio-Pardo, Karine Pires et al.

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit rate, aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-control strategies at the client side, we take in this paper a providers' perspective and propose solutions to improve user satisfaction by optimizing the encoding rates of the video sequences. We formulate an integer linear program that maximizes users' average satisfaction, taking into account the network dynamics, the video content information, and the user population characteristics. The solution of the optimization is a set of encoding parameters that permit to create different streams to robustly satisfy users' requests over time. We simulate multiple adaptive streaming sessions characterized by realistic network connections models, where the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results further show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness a mong users and to reduce network resource usage. We finally propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content.