基于5G网络的多描述神经视频编码的多路径自适应视频流
Multipath Adaptive Video Streaming with Multiple Description Neural Video Codec over 5G Networks
浏览论文内容
中文总结 AI 辅助
该研究针对5G网络下现有多路径视频系统交付脆弱导致QoE低的问题，提出NeuralMDC神经多描述视频编解码器，开发对应多路径流系统，实验表明其QoE提升26%-44%、视频质量最高升41.8%且卡顿率低于0.32%。
中文摘要 AI 辅助
5G网络采用多条无线信道，以满足新兴应用对带宽和高分辨率视频流不断增长的需求。然而，现有的多路径视频系统大多基于整体式编解码器设计，这类编解码器需要足够完整的块交付；或是基于分层编解码器设计，这类编解码器依赖于基础层的及时交付。在存在遮挡、切换和异构路径容量的快速变化5G环境下，我们发现现有编解码器的解码依赖关系会使多路径交付变得脆弱：关键视频数据的瞬时交付不足会直接触发卡顿并降低用户体验质量（QoE）。本文提出NeuralMDC，一种与动态5G网络多路径流协同设计的神经多描述视频编解码器。NeuralMDC将每个视频块编码为可独立解码且可相互优化的描述流，每个描述流覆盖整个块。该设计将多路径交付单元从依赖的数据包或层改为独立的块级流，因此缺失的流主要会降低质量，而非导致块无法解码。基于NeuralMDC，我们开发了一个用户空间多路径流系统，该系统以简单但有效的调度逻辑将描述流映射到异构5G路径。通过基于轨迹的仿真和实际5G实验，NeuralMDC相较于现有的整体式、分层式和神经流系统，将QoE提升了26%-44%，将视频质量提升了最高41.8%，并将卡顿率保持在0.32%以下。
英文摘要
5G networks employ multiple radio channels to meet growing demands for bandwidth and high-resolution video streaming for emerging applications. However, existing multipath video systems are largely designed around monolithic codecs, which require sufficiently complete chunk delivery, or layered codecs, which depend on timely base-layer delivery. Under fast-varying 5G conditions with blockage, handovers, and heterogeneous path capacities, we observe that decoding dependencies in existing codecs make multipath delivery fragile: transient under-delivery of critical video data can directly trigger stalls and degrade QoE. This paper proposes NeuralMDC, a neural multiple-description video codec co-designed with multipath streaming for dynamic 5G networks. NeuralMDC encodes each video chunk into independently decodable and mutually refinable description streams, each spanning the full chunk. This design changes the multipath delivery unit from dependent packets or layers to independent chunk-level streams, so missing streams primarily reduce quality rather than making the chunk undecodable. Built on NeuralMDC, we develop a user-space multipath streaming system that maps description streams to heterogeneous 5G paths with simple yet effective scheduling logic. Across trace-driven emulation and operational 5G experiments, NeuralMDC improves QoE by 26%-44% over existing monolithic, layered, and neural streaming systems, improves video quality by up to 41.8%, and keeps stall ratios below 0.32%.
