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Delivering videos under less-than-ideal network conditions without compromising end-users' quality of experiences is a hard problem. Virtually all prior work follow a piecemeal approach---either "tweaking" the fully reliable transport layer or making the client "smarter." We propose VOXEL, a cross-layer optimization system for video streaming. We use VOXEL to demonstrate how to combine application-provided "insights" with a partially reliable protocol for optimizing video streaming. To this end, we present a novel ABR algorithm that explicitly trades off losses for improving end-users' video-watching experiences. VOXEL is fully compatible with DASH, and backward-compatible with VOXEL-unaware servers and clients. In our experiments emulating a wide range of network conditions, VOXEL outperforms the state-of-the-art: We stream videos in the 90th-percentile with up to 97% less rebuffering than the state-of-the-art without sacrificing visual fidelity. We also demonstrate the benefits of VOXEL for small-buffer regimes like the emerging use case of low-latency and live streaming. In a survey of 54 real users, 84% of the participants indicated that they prefer videos streamed using VOXEL compared to the state-of-the-art.
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Text embedding models yield high-resolution insights into conceptual knowledge from short multiple-choice quizzes
We develop a mathematical framework, based on natural language processing models, for tracking and characterizing the acquisition of conceptual knowledge. Our approach embeds each concept in a high-dimensional representation space, where nearby coordinates reflect similar or related concepts. We test our approach using behavioral data from participants who answered small sets of multiple-choice quiz questions, interleaved between watching two course videos from the Khan Academy platform. We apply our framework to the videos' transcripts and the text of the quiz questions to quantify the content of each moment of video and each quiz question. We use these embeddings, along with participants' quiz responses, to track how the learners' knowledge changed after watching each video. Our findings show how a small set of quiz questions may be used to obtain rich and meaningful high-resolution insights into what each learner knows, and how their knowledge changes over time as they learn.
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- Award ID(s):
- 2145172
- PAR ID:
- 10662800
- Publisher / Repository:
- Springer/Nature
- Date Published:
- Journal Name:
- Nature communications
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- The DOI is not currently available.
