Enabling Real-time Neural Recovery for Cloud Gaming on Mobile Devices

TL;DR

This paper introduces a neural network-based system that leverages game states and partial decoding to recover lost or corrupted video frames in cloud gaming, aiming to meet strict latency requirements on mobile devices.

Contribution

It presents a novel holistic system combining game state extraction, modified decoding, and neural recovery to improve video frame restoration in cloud gaming.

Findings

Effective recovery of lost video frames demonstrated on iPhone 12 and laptop.

Game states significantly enhance recovery accuracy.

System maintains latency below 80 ms in tested scenarios.

Abstract

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design.