Octopus: In-Network Content Adaptation to Control Congestion on 5G Links

8th ACM/IEEE Symposium on Edge Computing

It is challenging to meet the bandwidth and latency requirements of interactive real-time applications (e.g., virtual reality, cloud gaming, etc.) on time-varying 5G cellular links. Today’s feedback-based congestion controllers try to match the sending rate at the endhost with the estimated network capacity. However, such controllers cannot precisely estimate the cellular link capacity that changes at timescales smaller than the feedback delay. We instead propose a different approach for controlling congestion on 5G links. We send real-time data streams using an imprecise controller (that errs on the side of overestimating network capacity) to ensure high throughput, and then adapt the transmitted content by dropping appropriate packets in the cellular base stations to match the actual capacity and minimize delay. We build a system called Octopus to realize this approach. Octopus provides parameterized primitives that applications at the endhost can configure differently to express different content adaptation policies. Octopus transport encodes the corresponding app-specified parameters in packet header fields, which the basestation logic can parse to execute the desired dropping behavior. Our evaluation shows how real-time applications involving standard and volumetric videos can be designed to exploit Octopus, and achieve 1.5–18× better performance than state-of-the-art schemes.

Yongzhou Chen
Yongzhou Chen
Ph.D. Candidate in ECE