Multipath Adaptive Video Streaming with Multiple Description Neural Video Codec over 5G Networks

Abstract

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%.

17 pages, including appendix; 23 figures and 3 tables. Accepted at the 34th IEEE International Conference on Network Protocols (ICNP 2026)