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%.

Real-time communication (RTC) applications demand not only low average latency but also tight tail-delay bounds to ensure smooth user experience. However, sudden fluctuations in wireless networks can cause in-flight packets to accumulate in bottleneck queues, delaying or invalidating subsequent frames. Traditional mechanisms focus on rate adaptation but largely overlook managing already enqueued packets that contribute to tail delay. We present Gecko, a lightweight end-to-network coordination mechanism that enables frame-aware queue flushing without requiring any in-network packet modification or protocol negotiation. Gecko-enabled routers monitor queuing delay and implicitly signal the sender, which then makes frame-skipping decisions and conveys flushing intent through minimal in-band RTP markings. This approach preserves end-to-end integrity and is broadly compatible with existing RTC applications. We evaluate Gecko via trace-driven simulations and real-world experiments. Results show that Gecko reduces average frame delay by 21.8% and cuts tail-delay frame ratios by 25% to 91%, demonstrating both its effectiveness and deployability in wireless RTC environments.

Resource Public Key Infrastructure (RPKI) aims to prevent prefix hijacking by providing secure mappings between IP prefixes and their authorized origin Autonomous Systems (ASes). In recent years, there has been notable growth in the deployment of RPKI and Route Origin Validation (ROV). Nonetheless, over 40% of the routes in the global routing table still lack the protection of RPKI. One of the critical reasons some networks are reluctant to deploy RPKI is the concern that some ROV-invalid routes may be legitimate, and filtering these routes will harm network service quality, especially affecting network connectivity.

QUIC gains significant attention due to its superior transmission performance, achieving widespread adoption in both academia and industry. To improve round-trip time (RTT) estimation, QUIC introduces the Host Delay field, enabling senders to exclude receiver-induced delays. Many delay-based congestion control algorithms (CCAs) rely on these RTT estimates to detect congestion and regulate sending rates. However, we find that malicious Host Delay values can distort RTT measurements, causing inappropriate rate adjustments by CCAs.In this paper, we investigate a new class of attacks leveraging maliciously crafted Host Delay values. To our knowledge, we are the first to analyze the vulnerability of Host Delay and present QUDIT, a universal receiver-driven attack targeting delay-based QUIC CCAs. Unlike prior attacks that presume full network queuing delays visibility and undetected injection capabilities, our attacker model only grants the adversary access as a standard QUIC receiver with limited knowledge of bottleneck conditions. We minimally modify the QUIC receiver to infer bottleneck queuing behavior in real time. Based on these inferences, we design dynamic Host Delay crafting strategies tailored to the specific behavior of various delay-based CCAs and accounting for random network fluctuations. Our attack prompts the sender to overshoot its rate, leading to excessive bandwidth consumption at the bottleneck and degradation of competing flows. Results demonstrate the throughput degradation of victim flow achieves up to 60% within 0.3 s and amplification gains between 200× and 600×. We propose defenses mitigating QUDIT, with vulnerabilities reported to IETF and QUIC maintainers.

Volumetric video streaming, an innovative media application, facilitates the real-time and immersive teleportation of individuals or objects into the virtual environment of the audience. Unlike conventional video streaming applications, volumetric content is particularly vulnerable to network fluctuations, which can lead to performance degradation such as reduced FPS, delayed frame delivery. In this research, we introduce an eBPF-based network function that duplicates packets along the pathways between network nodes, ensuring timely packet delivery amid network instability. Furthermore, we propose a path elimination algorithm to discard paths incapable of delivering frames within the target latency. Our implementation and evaluation validate the rapid and robust performance achieved across various resolution levels.

Mobile web services value a quick loading of contents in the first page, which is quantified by the above-the-fold time of the first page (first AFT) and is likely to fall into the slow start phase in congestion control. However, the widely deployed slow start mechanism is "cold start", which manually hardcodes the parameters and is not suitable for the first AFT of heterogeneous mobile web services. We revisit the slow start mechanism and find that it could be optimized with a priori knowledge. However, blindly relying on a priori knowledge is not robust enough to handle the fluctuating mobile networks and unpredictable application traffic. In this paper, we propose WiseStart, a "hot-start-based" slow start mechanism. WiseStart utilizes the priori knowledge to set the initial parameters, continuously probes the new connection to handle the fluctuating network conditions, and carefully adapts to the application-limit scenarios. We implement WiseStart in a popular mobile web service online in production. Comprehensive experiments demonstrate that WiseStart reduces the First AFT by 25.43% and the average RCT at connection establishment by 16.15% compared to the default slow start mechanism and other state-of-the-art baselines.

To improve the performance of mobile web services, a new transport protocol, QUIC, has been recently proposed as a substitute for TCP. However, with pros and cons of QUIC, it is challenging to decide whether and when to use QUIC in large-scale real-world mobile web services. Complex temporal correlation of network conditions, high user heterogeneity in a nationwide deployment, implementation diversity of QUIC variants limited, and resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans, an adaptive transport protocol selection mechanism, to switch transport protocols for mobile web services online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, adopts an online learning method to keep pace with implementation variation, and switches transport protocols at the request level to reach high performance with acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of Baidu. Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 25.8% on average compared to the usage of a single protocol.

To improve the performance of mobile web service, a new transport protocol, QUIC, has been recently proposed. However, for large-scale real-world deployments, deciding whether and when to use QUIC in mobile web service is challenging. Complex temporal correlation of network conditions, high spatial heterogeneity of users in a nationwide deployment, and limited resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests.

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile phones. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the deployment of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance, and scalable framework that can faithfully convert sophisticated ABR algorithms into decision trees with teacher-student learning. In this way, network operators can train complex models offline and deploy converted lightweight decision trees online. We also present theoretical analysis on the conversion and provide two upper bounds of the prediction error during the conversion and the generalization loss after conversion. Evaluation on three representative ABR algorithms with both trace-driven emulation and real-world experiments demonstrates that PiTree could convert ABR algorithms into decision trees with <; 3% average performance degradation. Moreover, compared to original deployment solutions, PiTree could save considerable operating expenses for content providers.