Optimizing quality of experience (QoE) for live video streaming (LVS) remains a long-standing challenge for content delivery network (CDN) providers. Today’s CDNs predominantly employ static congestion control (CC) configurations, yet the heterogeneity of LVS scenarios undermines the efficacy of a uniform CC solution applicable to all users, as evidenced by our production measurements. While learning-based CC approaches show promise, they often suffer from limited generalizability, non-transparent design, and high computational overhead, struggling in large-scale CDNs. In this paper, we propose BIFROST, a new CC architecture grounded in a concerndecoupling paradigm. BIFROST separates fixed control logic from scenario-specific parameter optimization, enabling CDN systems to adapt dynamically to diverse LVS scenarios. To materialize it at CDN scale, BIFROST introduces a bilateral collaboration mechanism that identifies the LVS scenarios of each session by extracting client-side characteristics from viewing requests. It further employs offline model-based optimization to derive more effective control parameters for each scenario. We have deployed BIFROST on Alibaba Cloud’s production CDN for nearly a year, serving a commercial LVS application. BIFROST has markedly improved QoE metrics by 2.7% to 32.1%, reinforcing the competitive edge in the CDN market. We also share our experiences and lessons learned from its large-scale deployment.

The escalating need for live video streaming has emerged as a significant catalyst for the business expansion of today’s content delivery networks (CDN). Selecting the right CDN live streaming architecture is fundamentally important in achieving the objective of enhancing users’ quality of experience (QoE) while reducing bandwidth costs. Regrettably, a limited number of studies have been conducted to systematically measure and compare the current typical solutions at production scale. Consequently, the performance and costs of different streaming architectures remain myths. This paper aims to address the existing research gap by undertaking a large-scale measurement study of three representative CDN live streaming architectures, defined by streaming protocol and overlay topology choices, currently running on Alibaba Cloud’s production video delivery network. By analyzing the results of over 500 million video plays over two months on a large live streaming platform hosted on Alibaba Cloud’s CDN, we reveal the impact of architectural compositions and operational factors on live streaming performance and bandwidth costs. In particular, our study reveals the trade-offs between QoE metrics and bandwidth costs for operational streaming architectures. Drawing upon the insights of this study, we further develop and deploy pragmatic strategies that yield remarkable real-world impact—our design saves over 17% bandwidth costs while maintaining the QoE.

With the widespread adoption of video streaming applications, effective video delivery solutions are crucial for providing seamless user experiences. Recent studies have revealed that multipath transmissions are beneficial to video streaming applications, given their potential of better load balancing and fault tolerance, relative to single path settings. However, the necessity of cross-layer co-design of multipath routing and video frame scheduling is overlooked. This work identifies that preset or path-oblivious frame scheduling used in existing works cannot adapt to network dynamics and fail to enhance the quality of experiences (QoE) in multipath transmissions. Therefore, we propose MPVSched, a novel framework that unifies the design of multipath routing and application-layer frame scheduling, with a particular focus on improving the rebuffer rate for short video delivery. At the network layer, we propose to use network-assisted routing that selects the optimal paths for each video transmission, with per-hop per-frame latency prediction. We implement an end-to-end QUIC-based video streaming system by integrating our routing strategy and application-layer frame scheduler, which effectively improves streaming efficiency and prevents user-side freezes. Our testbed experiments with real-world short video request traces demonstrate that MPVSched can achieve reductions of up to 28.58% in rebuffer ratio, compared to representative baseline methods.

The flow table is a critical component of Software-Defined Networking (SDN). However, flow tables’ limited capacity makes them highly vulnerable to flow table overflow attacks (FTOAs). Due to the low attack cost and highly flexible attack forms, it is hard to eradicate FTOAs. This paper addresses three unsolved problems for table security and proposes a robust defense accordingly. First, we reveal that the existing defenses with fixed defense speeds will cause severe packet loss when handling diverse traffic. We prove that deleting multiple rules can efficiently solve this problem and give a rigorous derivation to calculate the suitable deletion number according to the environment. Second, we illustrate that abnormal table occupancy squeezing is a constant characteristic of FTOAs regardless of attack forms. It can be used to identify attacked ports accurately in different scenarios. Third, we mathematically prove that random deletion can guarantee the continuous decrease of malicious flow rules after confirming attacked ports. It achieves fast speed and robust effectiveness in different environments. Based on these findings, we design rDefender, a robust and lightweight defense prototype. We evaluate its effect by designing diverse, powerful attacks and using real-world datasets and topology. The results demonstrate that it achieves the best overall performance compared to six existing mainstream defenses, providing stable security for switch flow tables.

Live video streaming has come to dominate today’s Internet traffic. Content Delivery Network (CDN) providers, responsible for hosting outsourced live streaming services, are now striving to ensure an enhanced quality of experience (QoE) to meet the ever-increasing user expectations. Existing congestion control (CC) schemes in the kernel, however, suffer from unsatisfactory performance for live video delivery due to disparities in traffic characteristics and differentiated optimization goals between generic traffic and live video traffic. In this paper, we propose XCC, a streaming context-aware CC approach that helps achieve better QoE for the live streaming services from CDN provider. The core of XCC is to adaptively coordinate the transmission strategy and frame rate through a cross-layer feedback framework, responding to the fluctuating traffic dynamics and network conditions in the short term. Further, XCC matches the long-term traffic characteristics (i.e., two-stage delivery mode) by employing a task-specific state transition mechanism as the underlying TCP. XCC has been implemented in the Linux kernel’s TCP stack and media engine and has been fully deployed in Alibaba Cloud’s production service. Evaluation in experimental environments and A/B testing serving tens of millions of sessions demonstrate that XCC is competitive in streaming delay against the most prevalent TCP in today’s Operating Systems, while reducing startup delay by 9.9%, stall time by 36.4%, and stall frequency by 42.5% on average in deployment.