Accelerating Distributed Optimization via Over-the-Air Computing

Distributed optimization is ubiquitous in emerging applications, such as robust sensor network control, smart grid management, machine learning, resource slicing, and localization. However, the extensive data exchange among local and central nodes may cause a severe communication bottleneck. To overcome this challenge, over-the-air computing (AirComp) is a promising medium access technology, which exploits the superposition property of the wireless multiple access channel (MAC) and offers significant bandwidth savings. In this work, we propose an AirComp framework for general distributed convex optimization problems. Specifically, a distributed primaldual (DPD) subgradient method is utilized for the optimization procedure. Under general assumptions, we prove that DPDAirComp can asymptotically achieve zero expected constraint violation. Therefore, DPD-AirComp ensures the feasibility of the original problem, despite the presence of channel fading and additive noise. Moreover, with proper power control of the users' signals, the expected non-zero optimality gap can also be mitigated. Two practical applications of the proposed framework are presented, namely, smart grid management and wireless resource allocation. Finally, numerical results reconfirm DPDAirComp's excellent performance, while it is also shown that DPD-AirComp converges an order of magnitude faster compared to a digital orthogonal multiple access scheme, specifically, time division multiple access (TDMA)

A Review of Deep Learning Solutions in 360° Video Streaming

The spread of virtual reality and 360° video applications has raised research interest in developing new streaming techniques. On one hand, 360° videos rely on strict network requirements compared to conventional 2D videos. Realizing an adequate user experience is subject to ultra-low latency and huge bitrate requirements. On the other hand, 360° videos have distinct characteristics that allow for innovative streaming solutions. These solutions have benefited from the advancements in deep learning for optimizing the transmission under restricted network resources. In this paper, we review existing works employing deep learning in 360° video transmission and we highlight the challenges associated with 360° video streaming

A Survey on Optimizing Mobile Delivery of 360^◦Videos:Edge Caching and Multicasting

Recently, there has been an ever-growing demand for virtual reality (VR) and 360° video applications. Different from conventional 2D videos, 360° videos take users into an immersive experience by providing them with a navigable panoramic view. However, achieving adequate quality of experience (QoE) levels poses significant network challenges, especially in mobile delivery setups. Despite the tremendous improvements offered by 5G and beyond mobile networks, streaming 360° videos in a similar fashion to 2D videos is suboptimal, while scaling at high numbers questions the feasibility of the endeavor. This paper explores the utilization of caching and multicasting solutions for the mobile delivery of VR and 360° videos. First, an overview of immersive technologies and their distinctive characteristics is provided. Then, we discuss the network challenges associated with 360° videos and the role of implementing robust caching and multicasting schemes that exploit the unique features of 360° videos and capitalize on the correlations among end-users’ viewports. Having established the foundations and challenges of 360° video streaming, we continue with a comparison of the state-of-the-art literature, while focusing on video streaming optimization aspects. We conclude our work by discussing the status and future research directions

Versatile Video Coding Performance Evaluation for Tiled 360° Videos

Recently, virtual reality (VR) and 360° videos have been increasingly adopted in a wide range of applications. However, the huge sizes of 360° videos necessitate the use of powerful compression tools to allow for efficient storage and streaming of these videos. In this article, we compare the performance of four different video codecs, including AV1 and versatile video coding (VVC), when applied to a dataset of 360° videos. We conduct our evaluation according to peak signal-to-noise ratio (PSNR)-based 360° video quality assessment (VQA) metrics. As omnidirectional videos are usually streamed as independent tiles, we also investigate the tiling effect on VVC compression performance by comparing full-frame-based compression to tiled segment compression using grids of 8x8 and 16x16 tiles