Video-on-demand (VoD) is one of the core CDN services, yet current VoD services commonly face video stuttering and long first-frame latency due to poor transmission path conditions. Existing solutions using Multi-path QUIC (MPQUIC) to address these issues cannot activate cellular paths on demand, causing massive traffic waste; additionally, some solutions depend on dedicated clients for application-layer information, precluding large-scale deployment. To address these limitations, we propose EMVOD, an elastic multi-path QUIC scheme deployed only on CDN edge nodes. It parses client-requested videos, dynamically activates cellular networks on demand, and prioritizes smooth video playback with minimal cellular traffic, while significantly reducing stuttering duration and first-frame latency.

Congestion control schemes based on information, such as queue size and traffic load, have become increasingly important, especially in data center networks, where the applications have stringent bandwidth and latency requirements. However, some congestion control schemes employ In-band Network Telemetry (INT), which introduces nontrivial bandwidth overhead. In this paper, we propose an efficient congestion control scheme: Precise ECN Encoding (PEE). PEE refactors the Explicit Congestion Notification (ECN) marking logic and proposes a novel ECN-based multi-packet joint encoding/decoding mechanism to enable more precise congestion perception. Compared with the state-of-the-art schemes, PEE achieves precise congestion control without introducing extra bandwidth overhead, achieving high throughput and low latency simultaneously. Comprehensive experimental results show that, compared to TIMELY, DCQCN, and HPCC, PEE reduces the average Flow Completion Time (FCT) by 102.1%, 26.8%, and 16.6% respectively under 80% network load.

In recent years, hybrid congestion control (CC) algorithms that combine rule-based CC and learning-based CC have gained significant attention. They incorporate the fast adaption ability of learning-based CC and the stability of rule-based CC, tending to select the better-performing rate based on the network feedback. However, the practical implementation of such algorithms has revealed primary issues. Specifically, they require both CCs to run alternately, which results in a poorly performing CC continuing to run in the network. Moreover, hybrid CCs cannot converge to the optimal rate when both CCs perform poorly. This paper proposes Anole to address these issues. Anole has three main algorithmic contributions: 1) Anole always selects the better-performing CC, 2) Anole temporarily deprecates the consistently underperforming CC, 3) when both CCs pervform poorly, Anole infers the optimal sending rate based on the network feedback. We carry out comprehensive experiments in both emulated and real-world wired networks, as well as in real-world WiFi networks, to assess the performance of Anole. The experiment results demonstrate that Anole achieves approximately 6% higher throughput in real-world links and 34% lower delay in the 48Mbps link compared to the state-of-the-art CC. Anole also exhibits superior performance in adaptability and fair convergence.