Improving Video Delivery with Fourier Analysis of Traffic in Multi-Access Edge Computing

Abstract

This paper presents a new video delivery scheme in mobile networks using Multi-Access Edge Computing (MEC). Our goal is to improve the quality of video streaming experienced by the mobile video consumer. Our approach is based upon Dynamic Adaptive Streaming over HTTP. We present a novel algorithm, which uses information obtained from the Radio Network Information Service of MEC to provide the mobile user with a video quality matching the current radio link quality and channel capacity. We evaluate our approach using a real experiment performed on a Long Term Evolution (LTE) femto cell test-bed. Our algorithm displays enhanced adaptation of video rates in comparison to other state of the art solutions.

Artificial Intelligence Aided Joint Bit Rate Selection and Radio Resource Allocation for Adaptive Video Streaming over F-RANs

“…To be specific, the ABR algorithms adaptively choose a bit rate for each video chunk, where the decision is based on the video content and network environments, such as the network throughput, playback buffer occupancy, and video chunk size [8]. For the conventional cloud-based network, the controller of the ABR is deployed on the client side (video player), and only selects a proper video bit rate without the ability of adaptive resource allocation [9]. In contrast, an F-RAN provides an additional action dimension, namely radio resource allocation on the physical layer, for ABR streaming service, which is radio resource allocation on the physical layer [1].…”