As the demand for streaming services surges, content delivery network (CDN) operators face increasing pressure to scale live video delivery without proportionally increasing infrastructure costs. While best-effort edge resources offer a cost-effective extension to traditional CDN capacity, their limited bandwidth and unstable performance pose significant challenges. Our operational experience shows that naively layering such resources onto existing CDN infrastructure falls short in meeting performance and scalability demands. This paper presents RLive, a robust delivery system that scales CDN capacity by integrating best-effort edge resources. RLive features a redundancy-free multi-source data plane to support reliable and cost-efficient live streaming, along with a multi-layer collaborative control plane that combines the global view with local adaptability for scalable user-to-node mapping. Deployed in ByteDance CDN to support large-scale live streaming services with hundreds of millions of daily viewers, RLive has tripled delivery capacity while reducing rebuffering events by 14.9–20.1%.

Today's fast-growing Video-on-Demand (VoD) service needs efficient content delivery to guarantee the user experience. To reduce costs, the industry has been exploring the adoption of unstable, heterogeneous, low-performance edge nodes as cost-efficient alternatives to expensive CDN servers. To compensate for the resulting degradation in user experience, Multi-source Parallel Downloading (MPD) is becoming a new VoD transport paradigm. However, existing transport optimization solutions face performance obstacles when applied to the MPD scenarios. They cannot handle the contention between MPD flows of the same download task, which is likely to occur at the shared last-hop, and lack the ability to quickly adapt to the unstable network environments brought by dynamic, heterogeneous, and low-performance edge nodes. To fill this gap, we propose Hermit, a VoD-oriented MPD transport algorithm. Hermit (1) continuously monitors the state of the flows and makes timely scheduling decisions, and (2) efficiently coordinates across the flows to mitigate self-contention at the shared last hop. As a client-driven scheme, Hermit does not require cumbersome coordination among edge nodes, nor does it increase server complexity. Through extensive experiments on real-world large-scale testbed and locally emulated network conditions, we demonstrate that Hermit can improve the consistent downloading rate by 9.2% to 21.3%.

Live streaming platforms like Douyin have developed the Live Streaming Hyper-Edge Delivery Network (LSHEDN) to reduce bandwidth cost. In LS-HEDN, the Content Delivery Network (CDN) splits the live streaming into multiple substreams by randomly assigning each frame to them. Hyperedge devices like set-top boxes with cheap and idle bandwidth resources forward a substream from CDN to multiple users. A protocol based on User Datagram Protocol (UDP) is adopted between devices and users, with users detecting packet loss and requesting retransmissions via Negative Acknowledgment (NACK). Given the demand for lower latency and the inherent fluctuations in public network, existing receiver-side packet loss detection and retransmission methods fall short in achieving both timeliness and accuracy simultaneously. This is manifested as frequent rebuffering and excessive redundancy. Notably, when head-of-line blocking(HOL blocking) occurs in the upstream link of the device, these issues become even more pronounced. To address this, we propose Hyper-Edge Loss Detection and Retransmission (HELDR) algorithm. It features a loss detection algorithm tailored to the transmission characteristics in LSHEDN, which improves detection accuracy. Its immediate retransmission mechanism and the backup devices retransmission mechanism enhance timeliness. Large-scale online A/B tests results show that HELDR reduces the average rebuffering rate by 41.2%, reduces the average redundancy rate by 15.7%.

With the exponential rise in video traffic, researchers and developers require more effective tools to validate the efficacy of designed algorithms for Video-on-Demand (VoD) system. However, traditional experimental platforms face two main challenges: a lack of realistic testing and the need for longer and significant effort. To overcome these limitations, we propose Magpie, an efficient experimental platform tailored for VoD systems. Magpie leverages a realistic operational setting, rapid testing, and high reproducibility to closely simulate online user environments without impacting production systems. Compared to conventional simulations, our evaluation demonstrates that Magpie reduces the disparity with online experiments by 85.6%. Deployed within our company-a leading video content provider in China-Magpie has efficiently validated over tens of algorithms, with 80% demonstrating enhanced performance in subsequent online tests.

Video content providers such as Douyin implement Peer-to-Peer Content Delivery Networks (PCDNs) to reduce the costs associated with Content Delivery Networks (CDNs) while still maintaining optimal user-perceived quality of experience (QoE). PCDNs rely on the remaining resources of edge devices, such as edge access devices and hosts, to store and distribute data with a Multiple-Server-to-One-Client (MS2OC) communication pattern. MS2OC parallel transmission pattern suffers from severe data out-of-order issues. However, direct applying existing schedulers designed for MPTCP to PCDN fails to meet the two goals of high aggregate bandwidth and low end-to-end delivery latency.To address this, we present the comprehensive detail of the Douyin self-developed PCDN video transmission system and propose the first QoE-enhanced packet-level scheduler for PCDN systems, called Pscheduler. Pscheduler estimates path quality using a congestion-control-decoupled algorithm and distributes data by the proposed path-pick-packet method to ensure smooth video playback. Additionally, a redundant transmission algorithm is proposed to improve the task download speed for segmented video transmission. Our large-scale online A/B tests, comprising 100,000 Douyin users that generate tens of millions of videos data, show that Pscheduler achieves an average improvement of 60% in goodput, 20% reduction in data delivery waiting time, and 30% reduction in rebuffering rate.