Enhancing the quality of experience (QoE) in interactive video streaming (IVS) remains a persistent challenge due to the need for ultra-low latency and rising bandwidth demands. Conventional algorithms, whether rule-based or learning-based, are obsessed with achieving tight coupling between encoding and sending bitrate adaptations for low-latency guarantee. However, our measurement studies reveal alarming harms of tight coupling in suppressing throughput, encoding bitrates and smoothness, as application- and transport-layer bitrate adaptations inherently have different mechanisms and goals. To tackle this problem, we propose Octopus, the first loosely coupled cross-layer bitrate adaptation algorithm for IVS to maximize QoE. Instead of blind synchronization, Octopus promotes mutual cooperation and independence between encoding and sending bitrate adaptations by integrating a multi-head network with shortcut connections and auto-regressive action modules. Additionally, based on meta-imitation reinforcement learning, we design a network condition-aware online adaptation scheme that enables the loosely coupled policy to swiftly adapt to diverse and dynamic wireless networks. We implement Octopus on a testbed, a microcosm of real-world deployment, with transceiver pairs running WebRTC on the WeChat for Business dataset. Results show that Octopus outperforms state-of-the-art algorithms, either improving bitrates by 37.1%, or optimizing stalling rate and smoothness by 54.1% and 9.2%, or achieving all-around improvements.

Real-time semantic segmentation (SS) is a major task for various vision-based applications such as self-driving. Due to the limited computing resources and stringent performance requirements, streaming videos from camera-embedded mobile devices to edge servers for SS is a promising approach. While there are increasing efforts on task-oriented video compression, most SS-applicable algorithms apply more uniform compression, as the sensitive regions are less obvious and concentrated. Such processing results in low compression performance and significantly limits the capacity of edge servers supporting real-time SS. In this paper, we propose STAC, a novel task-oriented DNN-driven video compressive streaming algorithm tailed for SS, to strike accuracy-bitrate balance and adapt to time-varying bandwidth. It exploits DNN's gradients as sensitivity metrics for fine-grained spatial adaptive compression and includes a temporal adaptive scheme that integrates spatial adaptation with predictive coding. Furthermore, we design a new bandwidth-aware neural network, serving as a compatible configuration tuner to fit time-varying bandwidth and content. STAC is evaluated in a system with a commodity mobile device and an edge server with real-world network traces. Experiments show that STAC can save up to 63.7–75.2% of bandwidth or improve accuracy by 3.1–9.5% compared to state-of-the-art algorithms, while capable of adapting to time-varying bandwidth.

Maximizing quality of experience (QoE) for interactive video streaming has been a long-standing challenge, as its delay-sensitive nature makes it more vulnerable to bandwidth fluctuations. While reinforcement learning (RL) has demonstrated great potential, existing works are either limited by fixed models or require enormous data/time for online adaptation, which struggle to fit time-varying and diverse network states. Driven by these practical concerns, we perform large-scale measurements on WeChat for Business’s interactive video service to study real-world network fluctuations. Surprisingly, our analysis shows that, compared to time-varying network metrics, network sequences exhibit noticeable short-term continuity, sufficient for few-shot learning requirement. We thus propose Fiammetta, the first meta-RL-based bitrate adaptation algorithm for interactive video streaming. Building on the short-term continuity, Fiammetta accumulates learning experiences through offline meta-training and enables fast online adaptation to changing network states through few gradient updates. Moreover, Fiammetta innovatively incorporates a probing mechanism for real-time monitoring of network states, and proposes an adaptive meta-testing mechanism for seamless adaptation. We implement Fiammetta on a testbed whose end-to-end network follows the real-world WeChat for Business traces. The results show that Fiammetta outperforms prior algorithms significantly, improving video bitrate by 3.6%-16.2% without increasing stalling rate.

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this article presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and wireless open-access research platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and above 90% in untargeted attacks with small perturbation-to-signal ratios of about −18 dB.