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%.

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.

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.