SEELE: Sense-Driven Edge-Cloud Foreground–Background Split Rendering for Immersive Media Services

Immersive media services increasingly rely on edge-cloud rendering to deliver interactive visual content under dynamic network, computing, and mobility conditions. Rendering an entire scene as a single service couples interaction-sensitive foreground content with context-oriented background content, making it difficult to jointly control latency, quality, synchronization, and migration overhead. This paper studies sense-driven edge-cloud foreground–background split rendering for immersive media services. We formulate an online decision problem in which foreground and background rendering layers can be independently controlled under long-term system and migration cost budgets. The formulation turns structural scene separation into a coupled layer-state control problem by preserving asymmetric QoE roles and a common composition requirement. We propose SEELE, a Lyapunov-guided online control algorithm that represents accumulated budget pressure with two virtual queues and converts the long-term constrained problem into lightweight per-slot decisions. The resulting per-slot rule balances immediate QoE loss against queue-weighted system and migration costs. Under sustained resource and network stress, SEELE provides steady-state QoE statistically comparable to a pretrained PPO policy while significantly reducing synchronization violations and improving composition stability. It also improves steady-state QoE and system debt over deterministic and QoE-prioritized baselines. A prototype implementation and controlled characterization further validate split-stream deployment, runtime observability, practical control hooks, and the latency–capacity tradeoff of layered rendering.

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Publication Details

Journal
Sensors
Published
2026-09-01
DOI
https://doi.org/10.3390/s26175561
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
0.00

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article

SEELE: Sense-Driven Edge-Cloud Foreground–Background Split Rendering for Immersive Media Services

Chuxing Fang, Xiao Han, Changqiao Xu, Enbo Wang et al.
Sensors
Image and Video Quality Assessment
article

SEELE: Sense-Driven Edge-Cloud Foreground–Background Split Rendering for Immersive Media Services

Chuxing Fang, Xiao Han, Changqiao Xu, Enbo Wang, Shaoyun Wu, Mingyu Zhao, Yuxuan Xiao
article en

Abstract

Immersive media services increasingly rely on edge-cloud rendering to deliver interactive visual content under dynamic network, computing, and mobility conditions. Rendering an entire scene as a single service couples interaction-sensitive foreground content with context-oriented background content, making it difficult to jointly control latency, quality, synchronization, and migration overhead. This paper studies sense-driven edge-cloud foreground–background split rendering for immersive media services. We formulate an online decision problem in which foreground and background rendering layers can be independently controlled under long-term system and migration cost budgets. The formulation turns structural scene separation into a coupled layer-state control problem by preserving asymmetric QoE roles and a common composition requirement. We propose SEELE, a Lyapunov-guided online control algorithm that represents accumulated budget pressure with two virtual queues and converts the long-term constrained problem into lightweight per-slot decisions. The resulting per-slot rule balances immediate QoE loss against queue-weighted system and migration costs. Under sustained resource and network stress, SEELE provides steady-state QoE statistically comparable to a pretrained PPO policy while significantly reducing synchronization violations and improving composition stability. It also improves steady-state QoE and system debt over deterministic and QoE-prioritized baselines. A prototype implementation and controlled characterization further validate split-stream deployment, runtime observability, practical control hooks, and the latency–capacity tradeoff of layered rendering.

SensorsVol. 26(17)
Beijing University of Posts and Telecommunications (CN), Huawei Technologies (China) (CN)
National Natural Science Foundation of China, State Key Laboratory of Networking and Switching Technology, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Reduced inequalities
Openalex Percentile: Top 13%
Image and Video Quality Assessment
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