Emerging low-latency video (LLV) services, such as cloud gaming, video conferencing, and virtual reality, demand ultra-low latency for smooth interaction. However, existing methods often overlook the misalignment between frame-level perception and packet-level scheduling in ubiquitous Wi-Fi networks, causing high tail latency and degraded user experience. To this end, we propose Flick, a frame-perceptive packet scheduling framework at Wi-Fi access points (APs). It leverages the periodic per-frame transmission behavior and the LLV traffic distribution characteristics to infer the end-to-end frame latency at APs. Flick consists of three components: a Frame Boundary Identifier that detects video frame boundaries using only packet size, an End-to-End Frame Latency Estimator that estimates the end-to-end latency without sender or receiver timestamps, and a Fast-Send, Slow-Recovery Scheduler that dynamically adjusts scheduling priority based on inferred latency. We implement Flick on a commercial Wi-Fi AP. Testbed results show that Flick reduces P99 latency and stall rate by 57% and 81%, respectively, while preserving 95% throughput and maintaining high fairness.

Latency is one of the most significant issues in cloud gaming, among which tail latency, mainly attributed to dynamic network environments (i.e., transmission) and limited device computing capacity (e.g., decoding), has attracted increasing attention. To mitigate the tail latency, different from existing researches considering resource adaptation such as bitrate adaptation, we propose TailClip, whose novel idea is to enable video frame generation at the client if the tail latency is about to happen. TailClip consists of two innovative components: a Deep Reinforcement Learning (DRL) driven tail latency trigger that jointly decides a series of subsequent frame generation regarding multidimensional features of historical tail latency; a lightweight frame generation model derived by adaptive pruning in terms of device computing capacity at runtime. Extensive evaluations indicate the superior performance of TailClip. For example, it can remove the high-latency frames (i.e., over 100 ms) by 77% with an acceptable video quality (i.e., 0.45 dB reduction on average).

360° videos generally require a large amount of bandwidth between video servers and users, which puts much burden on the current CDN-based single-source video streaming solutions. The emerging cloud native 5G networks can bridge the distance between video servers and users by leveraging in-network single-source video streaming to enhance 360° video quality. Unfortunately, the restricted bandwidth of in-network servers becomes the main bottleneck. Although tile-based video streaming is promising to reduce video transmission size while keeping user QoE, it highly depends on the accuracy of user FoV prediction, which existing prediction methods cannot guarantee. Recently, some researchers advocate the idea of “super FoV” (i.e., an extended range of predicted FoV) to cope with the inaccurate FoV prediction, which however could lower the effect of tile-based video streaming. Alternatively, we present Muster, a multi-source streaming for tile-based 360° videos within cloud native 5G networks. We detail the system components, provide a comprehensive model, formulate joint server selection and tile requesting problems, and correspondingly propose efficient online algorithms with a performance guarantee. Small-scale testbed and large-scale simulation based evaluation confirm the superiority of the proposed algorithms.

360° video streaming requires ultra-high bandwidth to provide an excellent immersive experience. Traditional viewport-aware streaming methods are theoretically effective but unreliable in practice due to the adverse effects of time-varying available bandwidth on the small playback buffer. To this end, we ponder the complementarity between the large buffer-based approach and the viewport-aware strategy for 360°video streaming. In this work, we present Sophon, a buffer-based and neural-enhanced streaming framework, which exploits the double buffer design, super-resolution technique, and viewport-aware strategy to improve user experience. Furthermore, we propose two well-suited ideas: visual saliency-aware prefetch and super-resolution model selection scheme to address the challenges of insufficient computing resources and dynamic user preferences. Correspondingly, we respectively introduce the prefetch and model selection metric, and develop a lightweight buffer occupancy-based prefetch algorithm and a deep reinforcement learning method to trade off bandwidth consumption, computing resource utilization, and content quality enhancement. We implement a prototype of Sophon and extensive evaluations corroborate its superior performance over state-of-the-art works.

The payment channel networks (PCNs), as the main method of blockchain off-chain expansion, have received extensive attention in recent years. Using the PCNs, two unconnected parties in a transaction can forward payments through existing payment channels of other nodes, which dramatically reduces interactions with the blockchain. But in large-scale dynamic PCNs, the routing mechanism is a challenge. Existing PCNs routing algorithms have some limitations. The landmark routing destroys the decentralization of blockchain. The static routing requires nodes having a global view and ultra-high computing power, which can not be applied to light nodes. We propose VEIN, a dynamic multi-path source routing algorithm, which is suitable for not only full nodes but also light nodes. We present an ingenious routing protocol, a modified max-flow algorithm to find edge-disjoint paths, and a path selection algorithm to deal with the NP-hard multi-path selection problem. Extensive experiments show that VEIN increases the transaction success ratio by 34% with the state-of-art algorithm, and realizes multiple orders of magnitude reduction in storage. In addition, we implement a prototype of VEIN on the Ethereum testnet to verify its feasibility.