Tile-Based Edge Caching for 360° Live Video Streaming

Abstract

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> video is becoming an increasingly popular technology on commercial social platforms and vital part of emerging Virtual Reality/Augmented Reality (VR/AR) applications. However, the delivery of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> video content in mobile networks is challenging because of its size. The encoding of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> video into multiple quality layers and tiles and edge cache-assisted video delivery have been proposed as a remedy to the excess bandwidth requirements of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> video delivery systems. Existing works using the above tools have shown promising performance for Video-on-Demand (VoD) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> delivery, but they cannot be straightforwardly extended in a live-streaming setup. Motivated by the above, we study edge cache-assisted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> live video streaming to increase the overall quality of the delivered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^{o}$ </tex-math></inline-formula> videos to users and reduce the service cost. We employ Long Short-Term Memory (LSTM) networks to forecast the evolution of the content requests and prefetch content to caches. To further enhance the delivered video quality, users located in the overlap of the coverage areas of multiple Small Base Stations (SBSs) are allowed to receive data from any of these SBSs. We evaluate and compare the performance of our algorithm with Least Frequently Used (LFU), Least Recently Used (LRU), and First In First Out (FIFO) algorithms. The results show the superiority of the proposed approach in terms of delivered video quality, cache-hit-ratio and backhaul link usage.