Privacy-Preserving Gaze-Assisted Immersive Video Streaming

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Mobile Computing Pub Date : 2024-08-30 DOI:10.1109/TMC.2024.3452510
Yili Jin;Wenyi Zhang;Zihan Xu;Fangxin Wang;Xue Liu
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Abstract

Immersive videos, also known as 360 $^{\circ }$ videos, have gained significant attention in recent years due to their ability to provide an interactive and engaging experience. However, the development of immersive video streaming faces several challenges, including privacy concerns, the need for accurate viewport prediction, and efficient bandwidth allocation. In this paper, we propose a comprehensive system that integrates three specialized modules: the Privacy Protection module, the Viewport Prediction module, and the Bitrate Allocation module. The Privacy Protection module introduces a novel approach to differential privacy tailored for immersive video environments, considering the spatial and temporal correlations in viewport and gaze motion data. The Viewport Prediction module leverages a crossmodal attention mechanism based on the transformer to predict user viewport movements by analyzing the complex interactions between historical data, video content, and gaze patterns. The Bitrate Allocation module employs an adaptive tile-based bitrate allocation strategy using an exponential decay function to optimize video quality and maximize user quality of experience. Experimental results demonstrate that our proposed framework outperforms three state-of-the-art integrated frameworks, achieving an average QoE improvement of 21.61%. This paper offers substantial novelty in addressing privacy concerns, leveraging gaze information for viewport prediction, and utilizing underlying correlations between different features.
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保护隐私的凝视辅助沉浸式视频流
身临其境的视频,也称为 360$^{\circ }$ 视频,近年来因其能够提供互动和引人入胜的体验而备受关注。然而,身临其境视频流的发展面临着一些挑战,包括隐私问题、准确视口预测的需求以及高效的带宽分配。在本文中,我们提出了一个综合系统,它集成了三个专门模块:隐私保护模块、视口预测模块和比特率分配模块。隐私保护模块考虑到视口和注视运动数据的空间和时间相关性,引入了一种为沉浸式视频环境量身定制的差异化隐私保护新方法。视口预测模块利用基于变换器的跨模态关注机制,通过分析历史数据、视频内容和注视模式之间的复杂交互来预测用户视口移动。比特率分配模块采用基于磁贴的自适应比特率分配策略,利用指数衰减函数优化视频质量,最大限度地提高用户体验质量。实验结果表明,我们提出的框架优于三个最先进的集成框架,平均提高了 21.61% 的 QoE。本文在解决隐私问题、利用注视信息进行视口预测以及利用不同特征之间的潜在相关性方面提供了实质性的创新。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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