Viewport Prediction via Adaptive Edge Offloading

Ahmet Gunhan Aydin;Haris Vikalo
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Abstract

The pursuit of enhanced interactive visual experiences has created growing interest in 360-degree video streaming. However, transmitting such content requires significant bandwidth compared to conventional planar video, motivating a search for effective bandwidth optimization strategies. A promising approach involves predicting viewport and prioritizing transmission of the regions of interest at higher quality. The existing methods for viewport prediction rely on sophisticated neural networks hosted on servers and face major bandwidth and latency challenges. This letter proposes a hierarchical approach to viewport prediction that leverages a small model on edge devices and offloads to the server only the most challenging tasks. The offloading algorithm relies on rate control to maximize the performance while meeting resource constraints, presenting a novel solution to bandwidth-efficient viewport prediction for 360-degree video streaming.
通过自适应边缘卸载的视口预测
对增强的交互式视觉体验的追求使人们对360度视频流越来越感兴趣。然而,与传统的平面视频相比,传输这样的内容需要显著的带宽,这促使人们寻找有效的带宽优化策略。一种很有前途的方法包括预测视口并以更高的质量优先传输感兴趣的区域。现有的视口预测方法依赖于托管在服务器上的复杂神经网络,并且面临着主要的带宽和延迟挑战。这封信提出了一种分层方法来预测视口,该方法利用边缘设备上的小型模型,并仅将最具挑战性的任务卸载给服务器。该算法在满足资源限制的前提下,通过速率控制实现性能最大化,为360度视频流的视口预测提供了一种新的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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