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.
Authors
- Chuxing Fang (ORCID: https://orcid.org/0009-0009-8589-3138)
- Xiao Han (ORCID: https://orcid.org/0000-0003-4219-7721)
- Changqiao Xu (ORCID: https://orcid.org/0000-0003-1467-1086)
- Enbo Wang (ORCID: https://orcid.org/0000-0002-5092-5138)
- Shaoyun Wu (ORCID: https://orcid.org/0009-0009-3279-9113)
- Mingyu Zhao (ORCID: https://orcid.org/0009-0000-1786-8189)
- Yuxuan Xiao
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Huawei Technologies (China) (CN)
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
Funders
- 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