Research & Insights


Conviaは、ストリーミングメディア向けのインテリジェンスクラウドです。 180カ国でのデバイス上の33億のアプリケーションにて視聴される1日当たり 1.8兆のリアルタイムイベントから、コンテンツ、ソーシャルメディア、広告配信における視聴体験を分析しています。

2021년 1분기 스트리밍 상태 보고서: 아시아 Recap 요약

Conviva는 미디어 스트리밍을 위한 지능형 클라우드입니다. 당사는 180개국의 33억 개의 디바이스에서 스트리밍되는 애플리케이션을 통해 매일 1조 8천억 개의 실시간 이벤트를 비롯하여 컨텐츠, 소셜 미디어, 광고 및 경험의 질을 분석합니다. 2021년 1분기 스트리밍 상태 아시아 보고서에서 당사는 아시아 지역의 스트리밍 산업…

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.

Redesigning CDN-broker interactions for improved content delivery

Various trends are reshaping Internet video delivery: exponential growth in video traffic, rising expectations of high video quality of experience (QoE), and the proliferation of varied content delivery network (CDN) deployments (e.g., cloud computing-based, content provider-owned datacenters, and ISP-owned CDNs).

A Case for End System Multicast

Dr. Zhang and Dr. Stoica have worked on new network architectures that enable direct control for routing data traffic, supporting network-level objectives, and providing network-wide visibility.

CFA: A Practical Prediction System for Video QoE Optimization

Many prior efforts have suggested that Internet video Quality of Experience (QoE) could be dramatically improved by using data-driven prediction of video quality for different choices (e.g., CDN or bitrate) to make optimal decisions. However, building such a prediction system is challenging on two fronts. First, the relationships between video quality and observed session features can be quite complex. Second, video quality changes dynamically. Thus, we need a prediction model that is (a) expressive enough to capture these complex relationships and (b) capable of updating quality predictions in near real-time.

C3: Internet-Scale Control Plane for Video Quality Optimization

As Internet video goes mainstream, we see increasing user expectations for higher video quality and new global policy requirements for content providers. Inspired by the case for centralizing network-layer control, we present C3, a control system for optimizing Internet video delivery.

Developing a Predictive Model of Quality of Experience for Internet Video

Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE.