Caching and reusing intermediate features across consecutive frames is a common technique to reduce redundant computation and transmission for edge-cloud video analytics in mobile edge computation. Existing methods manage the cache in a fixed or globally shifted coordinate system, treating it as an indivisible whole. Under the non-uniform motion patterns of mobile scenes, this whole-scene granularity invalidates large portions of the cache even when most content has merely shifted spatially, wasting computation and bandwidth. The root cause is a granularity mismatch: the cache is managed per scene, yet motion varies per region. In this paper, we present FluxShard, a motion-aware edge-cloud video analytics system that uses codec-level block motion vectors (MVs) to manage feature cache reuse and recomputation at the granularity of individual motion regions. By re-indexing cached features along per-block MVs, FluxShard separates spatial displacement from content changes, recovering reusable content that whole-scene methods would otherwise discard. To ensure correct reuse under heterogeneous motion, the Receptive Field Alignment Principle (RFAP) identifies, from the input-level MV field alone, the positions that must be recomputed due to inconsistent spatial composition within receptive fields. To maintain cache coherence across frames, MV-guided cache remapping warps the entire feature cache to the current coordinate system each frame, sustaining a high reuse ratio over time. A profiling-driven dispatcher routes the remaining sparse workload between edge and cloud for lower latency. Evaluation across multiple vision tasks, dynamic video benchmarks, and network conditions shows that FluxShard reduces latency by 32.6-83.8% and energy by 14.9-64.0% over all baselines under the prescribed accuracy budget.

翻译：在移动边缘计算场景中，跨连续帧缓存与复用中间特征是降低边缘-云端视频分析中冗余计算与传输的常用技术。现有方法采用固定或全局平移坐标系管理缓存，将其视为不可分割的整体。在移动场景的非均匀运动模式下，这种全场景粒度会导致即使大部分内容仅发生空间位移，缓存中大量数据仍失效，造成计算与带宽浪费。根本原因在于粒度失配：缓存按场景管理，而运动却具有区域差异性。本文提出FluxShard——一种运动感知的边缘-云端视频分析系统，利用编码器级块运动向量（MV）以单个运动区域为粒度管理特征缓存复用与重计算。通过沿逐块MV重建特征缓存索引，FluxShard将空间位移与内容变化解耦，恢复全场景方法原本丢弃的可复用内容。为确保异构运动下的正确复用，感受野对齐原则（RFAP）仅基于输入级MV场即可识别因感受野内空间组成不一致而必须重计算的位置。为维持跨帧缓存一致性，MV引导的缓存重映射每帧将整个特征缓存变形至当前坐标系，从而持续保持高复用率。基于性能剖析的调度器将剩余稀疏负载在边缘与云端间路由以降低延迟。在多个视觉任务、动态视频基准及网络条件下的评估表明，在预设精度约束下，FluxShard较所有基线方法降低32.6%-83.8%延迟和14.9%-64.0%能量消耗。