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%.

Modern real-time communication (RTC) demands both ultra-low latency and consistently high visual quality. Yet, as content becomes more dynamic and RTTs shrink, we reveal a previously overlooked problem: long-tail queuing latency in the sender's pacing queue between encoder and network. This phenomenon is rooted in a mismatch between the bursty frame stream produced by the encoder and the smooth traffic expected by the network. Existing approaches trying to smoothen the bitrate inevitably force an undesirable trade-off between latency and video quality. To address this, we propose a dual-control approach that manages both the encoding and transmission burstiness. At the sender, we dynamically adjust the bucket size of a token-based pacer to control burstiness at the granularity of frame level. Within the encoder, we introduce an adaptive complexity mechanism that smoothens frame sizes without sacrificing quality. Trace-driven emulation and real-world experiments show our solution ACE reduces end-to-end 95th percentile latency by up to 43% while maintaining superior visual quality versus the state of the art.

With the increase of diversity in application preferences and networks, existing congestion control algorithms (CCAs) do not accommodate this complicated reality. Previous classic CCAs are designed for a specific domain with fixed rules, failing to adapt to such diversities. Recently surged learning-based CCAs have great potential in adaptability and flexibility but are not practical due to unsatisfying performance on convergence, fairness, overhead, consistency and safety assurance. In this paper, we propose Libra, a unified congestion control framework, that can empower these properties by combining the wisdom of classic and reinforcement learning (RL)-based CCAs. Extensive evaluation of Libra’s Linux kernel implementations on both live Internet and emulated networks shows performance improvement under dynamic networks (e.g.,$1.2\times $throughput than Orca on average). At the same time, Libra can flexibly satisfy different application needs, reduce the running overhead by at most$0.88\times $and perform good fairness and convergence properties, well-fitting our theoretical analysis.

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.

Multipath TCP (MPTCP) is a burgeoning transport protocol which enables the server to transmit the traffic across multiple network interfaces in parallel. Classic MPTCPs have good friendliness and practicality such as relatively low overhead, but are hard to achieve consistent high-throughput and adaptability, especially for the ability to flexibly balance the congestion among different subpaths. In contrast, learning-based MPTCPs can essentially achieve consistent high-throughput and adaptability, but have poor friendliness and practicality. In this paper, we proposed MPLibra, a combined multipath congestion control framework that can complement the advantages of classic MPTCPs and learning-based MPTCPs together. MPLibra periodically leverages both classic MPTCPs and learning-based MPTCPs to make decisions and select the better one based on real-time network feedbacks. Extensive simulations on NS3 show that MPLibra can achieve good performance and outperform state-of-the-art MPTCPs under different network conditions. MPLibra improves the throughput by 40.5% and reduces the file download time by 29.94% compared with LIA, achieves good friendliness and balances congestion timely. What’s more, on the basis of MPLibra, we propose MPLibra+ which adds a safety module and an optimized packet scheduler and is the upgrade version of MPLibra. MPLibra+ has better ability to cope with untrained network environment and achieve better performance on heterogeneous scenarios compared with MPLibra.

Ultra-low latency video streaming has received explosive growth in the past few years. However, existing methods all focus on single-path transmission, which is ineffective in dealing with really poor network conditions. To tackle their problems, we propose TwinStar, a novel multi-path framework to improve the experience quality of ultra-low latency video. The core idea of TwinStar is to concurrently leverage multiple paths to mitigate the negative impacts of network jitter on a single path. In particular, by carefully designing the video encoding, data allocation and loss recovery, TwinStar is very robust to handle network dynamics and deliver high-quality video services. We have deployed TwinStar in a commercial cloud gaming platform and evaluated it with real-world networks. The extensive experiments demonstrate that TwinStar significantly outperforms the single-path transmission methods, with 91% reduction in stall ratio and 11% improvement in PSNR across all regions.

With the increase of diversity in application needs and networks, existing congestion control algorithms (CCAs) do not accommodate this complicated reality. Previous classic CCAs are designed for a specific domain with fixed rules, failing to adapt to such diversities. Recently surged learning-based CCAs have great potential in adaptability and flexibility but are not practical due to unsatisfying performance on convergence, fairness, overhead and safety assurance. In this paper, we propose Libra, a unified congestion control framework, which empowers flexibility, adaptability, and practicality, by combining the wisdom of classic and reinforcement learning (RL)-based CCAs. Extensive evaluation of Libra's Linux kernel implementations on both live Internet and emulated networks shows performance improvement under dynamic networks (e.g., 1.2x throughput than Orca on average). At the same time, Libra can flexibly satisfy different application needs, reduce the running overhead by at most 0.92x and perform good fairness and convergence properties, well-fitting our theoretical analysis.

Multipath TCP (MPTCP) is a burgeoning transport protocol which enables the server to split the traffic across multiple network interfaces. Classic MPTCPs have good friendliness and practicality such as relatively low overhead, but are hard to achieve consistent high-throughput and adaptability, especially for the ability of flexibly balancing congestion among different paths. In contrast, learning-based MPTCPs can essentially achieve consistent high-throughput and adaptability, but have poor friendliness and practicality. In this paper, we proposed MPLibra, a combined multipath congestion control framework that can complement the advantages of classic MPTCPs and learning-based MPTCPs. Extensive simulations on NS3 show that MPLibra can achieve good performance and outperform state-of-the-art MPTCPs under different network conditions. MPLibra improves the throughput by 40.5% and reduces the file download time by 47.7% compared with LIA, achieves good friendliness and balances congestion timely.