The rising crowdsourced live video streaming (CLVS) poses great challenges to Internet transport scalability, where a broadcaster’s live video is expected to reach thousands and even millions of viewers in real time. To accommodate such huge concurrent video traffic, the de-facto solution is to employ content delivery network (CDN), which distributes the traffic spatially relative to end viewers, using geographically distributed servers. However, our measurement study over a top operational CLVS platform reveals that CDN is not scalable enough, i.e., it loses efficacy, particularly duringbusy timeand leads to tremendous QoE degradation, e.g., 33.3% video bitrate reduction, in comparison to networkidle time. In this work, we propose Spider, which revives the peer-to-peer (P2P) networking principle to extend the scalability of CLVS system. Beyond traditional P2P for elastic data transmission, Spider retrofits P2P to meet the stringent low-latency requirements of CLVS: proposing a “pair-push” streaming mode to tame the excessive signaling latency; designing a QoE-driven peer pairing algorithm to tackle the Internet path variation and CLVS viewer dynamics. We implement, deploy and evaluate Spider in real-world over 20.9 thousand video sessions. Compared to the de-facto CDN solution, Spider achieves remarkable gains, e.g., video stall rate reductions of 52.57%, video quality gains of 8.22%, and even 66% CDN bandwidth saving. The results validate the feasibility and practicability of embracing P2P for low-latency live video communication for the first time.

While 5G has rolled out since 2019 and exhibited versatile advantages, its performance under high/extreme mobility scenes (e.g., driving, high-speed railway or HSR) remains mysterious. In this work, we carry out a large-scale field-trial campaign, taking >13,000 Km round-trips on HSR moving at 250–350 Km/h, with operational 5G cellular coverage along the railway. Our empirical study reveals that coupling interaction among high mobility, 5G handover characteristics, and applications’ sluggish reaction to handover, results in catastrophic damage to user experience: low TCP bandwidth utilization of 26.6% and glitchy 4K VoD streaming. To solve the problem, we propose an edge-assisted mobility management framework called Octopus. Different from previous works, Octopus aims at a standard-compatible and easy-to-deploy solution, thus we take a new design paradigm of exploiting the edge intelligence on multi-access edge computing (MEC). We realize Octopus as a universal MEC service ready for benefiting any third-party mobile applications. We prototype, deploy, and evaluate Octopus in operational 5G, which demonstrates the significant performance gain across the full-range mobile scenarios, e.g., HSR, driving, and walking.

Adaptive bitrate (ABR) algorithm is deployed extensively in commercial video delivery platforms, aiming to ensure users' quality of experience(QoE). Among the majority of existing ABR algorithms, throughput prediction plays a critical role. However, these predictors suffer from neglecting the throughput inconsistency across diverse chunk payloads under the network dynamics, e.g., the actual throughput of downloading a 4K or a 720P chunk is usually different, even when starting from the same moment. In this paper, we propose a payload-aware adaptive algorithm called PAR, which predicts multiple throughput estimations for different target payloads, and utilizes them to make better bitrate adaptation decisions. Trace-driven experiments show that PAR outperforms the existing ABR schemes across diverse network conditions, with the average QoE improvement of 2.66% to 79.43%.

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to support demanding applications with stringent latency requirements. The basic idea is to deploy servers close to end-users, e.g., on the network edge-side instead of the remote cloud. While conceptually reasonable, we find that the operational 5G is not coordinated with MEC and thus suffers from intolerable long response latency. In this work, we propose Tutti, which couples 5G RAN and MEC at the user space to assure the performance of latency-critical video analytics. To enable such capacity, Tutti precisely customizes the application service demand by fusing instantaneous wireless dynamics from the 5G RAN and application-layer content changes from edge servers. Tutti then enforces a deadline-sensitive resource provision for meeting the application service demand by real-time interaction between 5G RAN and edge servers in a lightweight and standard-compatible way. We prototype and evaluate Tutti on a software-defined platform, which shows that Tutti reduces the response latency by an average of 61.69% compared with the existing 5G MEC system, as well as negligible interaction costs.

This paper presents a novel time series classification algorithm. It exploits time-delay embedding to transform time series into a set of points as a distribution, and attempt to classify time series by classifying corresponding distributions. It proposes a novel geometrical feature, i.e. characteristic subspace, from embedding points for classification, and leverages class-weighted support vector machine (SVM) to learn for it. An efficient boosting strategy is also developed to enable a linear time training. The experiments show great potentials of this novel algorithm on accuracy, efficiency and interpretability.