Escape Cache Traps by Rate Feedback for Ndn Real-Time Video Streaming
Zhaohua Zhu, Yongrui Chen, Linggang Li, Zhijun Li, Weizhe Zhang, Yu Zhang · 2024
In-network caching is one of the most important characteristic of Named Data Networking (NDN). However, while replacing producers in responding to interest requests, caching data packets also shields consumers from perceiving the bottleneck bandwidth of the transmission path between the producer and the consumer. Therefore, when the content source switches from the cache node to the producer due to data exhaustion, the consumer can not adjust the requesting rate accordingly, and may lead to the serious bufferbloat or packet loss - we call it as Cache Trap. We found that Cache Trap occurs commonly in streaming services and the state-of-art NDN congestion control schemes cannot achieve efficient and stable quality of service when it happens. To escape Cache Trap, this paper proposes an explicit rate feedback congestion control algorithm, named as RFCC. RFCC leverages NDN routers' ability of encapsulating customized information in data packets to send link state information to consumers. Specifically, when responding to interest packets, RFCC nodes estimate data throughput received from the producer and insert this information into the returned data packets. The consumer perceives the change of content source according to the hopcount tag in data packet, and then adjusts the sending rate of interest packet based on the explicit rate information. We have implemented RFCC in both real-world NDN live video streaming and NDNsim simulation platforms, and compared it with the state-of-arts congestion control algorithms in a variety of scenarios. The experimental results show that when Cache Trap occurs, RFCC maintains a stable QoE in live video streaming, reduces 50% delay jitters compared with DPCCP and achieves$2.4 \times$throughput compared with PCON.