Two emerging trends of Internet applications, video traffic becoming dominant and usage-based pricing plans becoming prevalent, are at odds with each other. On one hand, videos, especially on high-resolution devices (e.g., iPhone 5, iPad, Android tablets), consume much more data than other types of traffic; for instance, 15 min of low bitrate YouTube videos per day uses 1 GB a month. On the other hand, gone are the days of unlimited data plans; instead, wireless ISPs such as AT&T and Verizon are imposing data caps on consumers. Given this conflict, a natural question to ask is: Can the consumer stay within her monthly data quota without suffering a noticeable drop in video quality? My research in this area focuses on designing algorithms to maximize the user's quality of experience and stay under the data quota, by leveraging the user's past data consumption profile and video preferences.

Publications

Chen J, Ghosh A, Magutt J, Chiang M, "QAVA: Quota-Aware Video Adaptation", CoNEXT 2012 (slides)

Edge Artificial Intelligence and Machine Learning

• Secure Machine learning
• Edge AI Test Bed
• Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning.
• Robotic Intervention Learning at the Edge

Fog Computing

• Clouds with Deadlines
• Completion-time-aware Scheduler
• Securing Tor
• Multi-resource fairness
• LTE Multicast
• Massive Machine-Type Communications
• Coded Computing
• Client Control of HetNets
• Shred and Spread
• Big Data Sharing
• Networked Drone Cameras

Smart Data Pricing

• Time-dependent Pricing
• Cloud Bidding
• Sponsored Data
• Cloud Virtual Service Provider
• Traded Data Plans
• Quota-aware Video Adaptation

Social Learning Networks

• Personalized Thread Recommendations for MOOC Forums
• Individualization
• Engagement Modeling
• Clickstream Analytics
• Social Learning Efficiency