Jibin Zhan

Understanding the impact of video quality on user engagement

As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events. We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.

On-demand waypoints for live P2P video broadcasting

A peer-to-peer architecture has emerged as a promising approach to enabling the ubiquitous deployment of live video broadcasting on the Internet. However the performance in these architectures is unpredictable and fundamentally constrained by the characteristics of the members participating in the broadcast. By characteristics, we refer to user dynamics, out-going bandwidth connectivity, whether the member is behind NAT/firewall, and the network conditions among participating members. While several researchers have looked at hybrid P2P/CDN approaches to address these issues, such approaches require provisioning of centralized server resources prior to a broadcast, which complicates the goal of ubiquitous video broadcasting. In this paper, we explore an alternative architecture where users are willing to donate their bandwidth resources to a broadcast event, even though they are not a participant in the event. Such users constitute what we term a waypoint community. Any given broadcast event involves constructing overlays only based on participants to the extent possible, however waypoints may be dynamically invoked in an on-demand, performance-driven fashion to improve the performance of a broadcast. We present the design of a system built on this idea. Detailed results from trace-driven experiments over the PlanetLab distributed infrastructure and Emulab demonstrate the potential of the waypoint architecture to improve the performance of purely P2P-based overlays.

Oboe: auto-tuning video ABR algorithms to network conditions

Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.