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%.

Mobile real-time video streaming (RTVS) demands ultra-low latency to preserve content timeliness. Packet loss in mobile networks significantly inflates frame latency and thus degrades the quality of experience (QoE). As a promising solution, Forward Error Correction (FEC) encoding has been widely deployed in RTVS systems to recover from packet loss by introducing redundancy. However, existing schemes focus on per-frame FEC protection, failing to optimize QoE because they cannot precisely allocate redundancy to handle burst loss events. These events typically occur at the single-frame level, but can be smoothed out at the multi-frame level. We propose Breath, an adaptive FEC scheme that dynamically adjusts the protection boundary based on network and video dynamics. We have implemented Breath in a RTVS system and evaluated it in emulated mobile networks using network traces collected from the production system. Results show that, compared to state-of-the-art FEC schemes, Breath reduces deadline missing rate by 17.2%-22.5% while improving the average video bitrate by 10.6%-14.2%.

To satisfy the unprecedented Quality of Experience (QoE) and stringent latency Service Level Agreements (SLAs) of emerging interactive applications, modern Content Delivery Networks (CDNs) are deploying massively decentralized edge nodes. However, this paradigm shift poses a significant challenge: optimal traffic scheduling fundamentally depends on acquiring real-time, full-coverage end-to-end path latency data to prevent SLA violations. Current measurement methods cannot scale to monitor every possible user-to-node path, and traditional prediction approaches (e.g., relying on additional segmented measurements or low-rank matrix decomposition) fail to achieve satisfactory accuracy on the resulting extremely sparse datasets. In this work, we present ADePT (Application Delay PredicTion), a novel data-driven causal inference framework that provides comprehensive and precise latency predictions without requiring additional measurements. By explicitly decoupling user-side temporal variations (e.g., last-mile congestion) and node-side spatial variations (e.g., core propagation delays), ADePT successfully extracts high-dimensional latent embeddings from limited measurement data to infer the end-to-end path latency for any potential scheduling decision. Evaluated on a massive real-world dataset from a leading edge CDN, ADePT reduces prediction errors by 19.6% and achieves a median absolute error of 4.3 ms. Consequently, integrating ADePT’s accurate predictions into CDN traffic scheduling significantly improves scheduling decisions, increasing the ratio of traffic meeting strict applications’ latency requirements by 1.66 × .

Low-latency cloud rendering enables mobile users to experience high-quality, real-time 3D graphics but achieving low Motion-to-Photon (MTP) latency while maintaining smooth playback is a significant challenge. Our real-world measurement study identifies Receive-to-Composition (R2C) latency, caused by ineffective jitter buffer management, as the primary factor contributing to increased MTP latency. To address this, we introduce JitBright, a client-side optimization strategy that dynamically reduces MTP latency through adaptive jitter buffer management. By adjusting buffer levels based on smoothing playback probability and implementing proactive keyframe requests to mitigate frame dependency, JitBright minimizes both active and passive waiting times. Our large-scale evaluation, conducted over 591,000 sessions across diverse network conditions (WiFi, 4G, 5G) and device types, demonstrates significant improvements in user experience. JitBright reduces median R2C latency by up to 87.5%, increases the proportion of sessions meeting strict MTP latency requirements by 6%–27%, and decreases the video freeze rate from 2.4%–2.8% to 0.4%–1.0%.

Real-time video (RTV) systems place high demands on ultra low-latency (i.e., less than 100 ms). However, our large-scale measurements reveal that a significant portion of users still experience high video frame latency due to bandwidth jitters. Existing solutions attempt to mitigate this issue by lowering the sender's future video frame encoding bitrate. Nevertheless, as shown in our controlled experiments, they fail to drain existing packets queued on the bottleneck node (i.e., the 5G base station and Wi-Fi access point), still suffering from high tail latency as bandwidth decreases. In this paper, we propose Co-RTV, a collaborative RTV system that achieves predictable latency control. Specifically, Co-RTV enables endpoint-network collaboration between the bottleneck node and the sender. The collaboration speeds up the release of packets queued at the bottleneck node and facilitates accurate latency control at the RTV sender through scalable QoE-driven flow control. Extensive experiments in emulated networks and on a 5G testbed demonstrate the superior performance of Co-RTV, with tail latency reductions of 69.1% and 70.5%, respectively.

In real-time communication (RTC) systems, accurate bandwidth prediction is crucial for encoding and transmission strategies to optimize users' quality of experience (QoE) in various network environments. In this paper, we propose an offline reinforcement learning (RL) method to predict bandwidth for RTC video streaming. We use a representative algorithm, named Implicit Q-Learning (IQL), to train the model. To improve the performance, we carefully preprocess the given dataset and redesign the neural network structure and the reward function. Ablation studies are performed to verify our design choices. Furthermore, compared to a baseline method and six behavior policies, our method reduces the mean squared error (MSE) by 18%-22%, demonstrating high prediction accuracy. Our proposed method won the first prize in ACM MMSys 2024 Grand Challenge on Offline Reinforcement Learning for Bandwidth Estimation in Real Time Communications. The source code is available at https://github.com/n13eho/Schaferct.

Increasing bandwidth demands of mobile video streaming pose a challenge in optimizing the Quality of Experience (QoE) for better user engagement. Multipath transmission promises to extend network capacity by utilizing multiple wireless links simultaneously. Previous studies mainly tune the packet scheduler in multipath transmission, expecting higher QoE by accelerating transmission. However, since Adaptive BitRate (ABR) algorithms overlook the impact of multipath scheduling on throughput prediction, multipath adaptive streaming can even experience lower QoE than single-path. This paper proposes Chorus, a cross-layer framework that coordinates multipath scheduling with adaptive streaming to optimize QoE jointly. Chorus establishes two-way feedback control loops between the server and the client. Furthermore, Chorus introduces Coarse-grained Decisions, which assist appropriate bitrate selection by considering the scheduling decision in throughput prediction, and Finegrained Corrections, which meet the predicted throughput by QoE-oriented multipath scheduling. Extensive emulation and real-world mobile Internet evaluations show that Chorus outperforms the state-of-the-art MPQUIC scheduler, improving average QoE by 23.5% and 65.7%, respectively.

Low-latency cloud rendering services use high-performance servers to provide mobile device users with exquisite graphics and convenient access experiences. Due to the complexity of the system and the diversity of impacting factors, identifying system bottlenecks has become a significant challenge. To demystify system performance, we build an online cloud rendering system to measure the latency distribution of its key components.

Video streaming is the most important mobile application today. To improve users’ quality of experience (QoE), the client player runs adaptive bitrate (ABR) algorithms that dynamically select the bitrate for video chunks based on throughput or delivery time predictions. This paper aims to design an accurate predictor for mobile adaptive streaming by investigating all its components, including input features, output target, and mapping function. We construct the first theoretical framework that reveals potential factors affecting chunk throughput and delivery time. To verify this framework, we provide formulation analysis and measurement observations based on 2500+ video sessions collected in real-world mobile networks. We find that previous works have failed to achieve accurate prediction due to overlooking the impact of the transport mechanism and application behavior on throughput. Furthermore, we show that throughput is a better target for data-driven predictors than delivery time, due to the long-tailed distribution of delivery time. Based on the above, we propose Lumos, a decision-tree-based throughput predictor that can be integrated into various ABR algorithms. Extensive experiments in real-world mobile Internet show that Lumos achieves high prediction accuracy and improves the QoE of MPC by 6.3%, and MPC+Lumos outperforms Pensieve by 19.2%.

ABR algorithms dynamically select the bitrate of chunks based on the network capacity. To estimate the network capacity, most ABR algorithms use throughput prediction while recent works start to leverage delivery time prediction. We in this paper examine all components of the predictor for ABR algorithms, i.e., input features, mapping function and output target. We build an automated video streaming measurement platform, and collect extensive dataset under various network environments, containing 2500+ video sessions. Through analysis, we find that most of previous works failed to achieve accurate prediction due to ignoring how application behavior influences application throughput, e.g., the strong correlation between chunk size and throughput. Then we identify underlying factors affecting this correlation, and consider them as features for more accurate prediction. Moreover, we show that throughput is a better target for data-driven predictors than delivery time in terms of prediction error, due to the long tail distribution of delivery time. Based on those above, we propose a decision-tree-based throughput predictor, named Lumos, which acts as a plug-in for ABR algorithms. Extensive experiments in real-world Internet demonstrate that Lumos achieves high prediction accuracy and improves the QoE of ABR algorithms when integrated into them.