Adaptive bitrate streaming (ABR) and quality of experience (QoE) metrics are proposed to enhance video streaming quality across various Internet connections. Traditional approaches to evaluating these metrics often ignore common user behaviors like seeking, jumping, or replaying video segments, leading to gaps in QoE understanding. Addressing this, we collected 229,178 audience retention curves from YouTube and Bilibili, offering a thorough view of viewer engagement and diverse watching styles. Our analysis reveals notable behavioral differences across countries, categories, and platforms. The YouTube data highlights varied content preferences, such as gaming and entertainment in some countries, and music, travel, and pets & animals in others. Additionally, Bilibili shows trends of early video abandonment, possibly influenced by platform-specific factors and shorter video formats. This enhanced grasp of user engagement aids in refining ABR and QoE metrics. We also highlight several potential applications of our dataset.

Recent research has proposed that Content Delivery Networks (CDNs) can use better bandwidth allocation to improve video streaming services through congested links. Because CDNs are usually not located at the bottleneck link, shared bottleneck (co-bottleneck) detection on the video servers is necessary for joint flow shaping and the Quality of Experience (QoE) improvements. However, co-bottleneck detection is challenging in such environments due to the large number of flows, possible network topologies, and traffic patterns. Current detectors fail to balance detection accuracy, speed and overhead, and suffer performance degradation in the scale of thousands of flows on each video server. We propose FlowBot, a novel model-based passive co-bottleneck detector designed for deployment on a video server. FlowBot uses Siamese model to learn flow representations, and combines the training procedure with its clustering algorithm to continue to provide strong performance with up to thousands of flows. Our evaluations show that FlowBot can achieve consistently high accuracy (over 70% F1 with around 90% precision) in most tested scenarios, while maintaining a short detection delay of 3 s and overhead similar to the fastest benchmark algorithms.

Internet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20%--30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele.

Digital Contact Tracing (DCT) has been proposed to limit the spread of COVID-19, allowing for targeted quarantine of close contacts. The protocol is designed to be lightweight, broad-casting limited-time tokens over Bluetooth Low Energy (BLE) beacons, allowing receivers to record contacts pseudonymously. However, currently proposed protocols have vulnerabilities that permit an adversary to perform massive surveillance or cause significant numbers of false-positive alerts. In this paper, we present AcousticMask, which encrypts broadcast messages using a key derived from the audio signal present at each device with sufficient security levels. Our results show that a receiver sharing the same social space as a sender will hear all of the sender's ephemeral IDs (EphIDs) with Hamming distance at most 3, which can be decrypted at the rate of 10 Hz on a Raspberry Pi 4, while achieving a security factor of over 2108against attackers in our testing set, showing AcousticMask is lightweight for DCT and provides sufficient security levels to protect user's privacy.

Due to the COVID-19 pandemic, many researchers have proposed privacy-preserving smartphone proximity tracing. Current projects, based on ephemeral IDs, are vulnerable to DoS attacks. In this paper, we present BlindSignedIDs that can be verified in-place through a TESLA server. We will demonstrate our BlindSignedIDs can effectively mitigate such DoS attacks.