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Chandan Bothra

dblp:278/3166 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2026
0009-0006-9674-4646ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 3 · 2 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Content delivery and video streaming · 83% Network performance modeling · 8% Wireless networking · 8%
Computer graphics and multimedia
1 paper
Multimedia systems and quality of experience · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia systems and quality of experience
video quality assessment
0.712023
Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback · SIGCOMM 2023
Content delivery and video streaming
360-degree video streaming
0.712023
Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback · SIGCOMM 2023
Content delivery and video streaming
adaptive video streaming
0.712023
Veritas: Answering Causal Queries from Video Streaming Traces · SIGCOMM 2023
Content delivery and video streaming › 360-degree video streaming
viewport-adaptive streaming
0.712023
Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback · SIGCOMM 2023
Wireless networking › channel assignment › wireless resource management
bandwidth adaptation
0.212023
Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback · SIGCOMM 2023

Methods — techniques the papers use, named apart from their topics

viewport prediction · 1.3tile-based spatial segmentation · 1.3domain-specific ML model · 0.7causal inference · 0.7abduction · 0.7
YearPublicationVenuePosition
2026 Beagle: Auto-tuning Performance Diagnosis for Live Video Streaming
Chandan Bothra, Leonardo Teixeira, Zahaib Akhtar, Sanjay G. Rao, Bruno Ribeiro 0001
IWQoS1
2023 Veritas: Answering Causal Queries from Video Streaming Traces
abstract
In this paper, we consider the task of answering what-if questions in the context of adaptive bit rate (ABR) video streaming without access to randomized control trials (RCTs) (e.g., no A/B testing) - i.e., given recorded data of an existing deployed system, what would be the performance impact if we changed its design. Our work makes three contributions. First, we show the problem is challenging since data may only be available for a single ABR algorithm without RCTs, and since it is necessary to deal with the cascading effects that past ABR decisions have on future decisions. Next we present Veritas, the first framework that tackles causal reasoning for video streaming without requiring data collected through RCTs. Integral to Veritas is an easy-to-interpret domain-specific ML model that relates the latent stochastic process (intrinsic bandwidth that the video session can achieve) to actual observations (download times), while exploiting counterfactual queries via abduction using the observed TCP states (e.g., congestion window) for blocking the cascading dependencies. Third, we evaluate Veritas's ability to accurately answer a wide range of what-if questions using emulation experiments, and data of real video sessions from Puffer. The results show that (i) Veritas accurately tackles a wider range of what-if questions (e.g., change of buffer size or video quality) that existing approaches cannot; (ii) Veritas without RCT training data achieves performance comparable or better than a recent parallel approach that requires RCT data; and (iii) in many scenarios Veritas achieves accuracy close to an ideal oracle.
Chandan Bothra, Jianfei Gao 0001, Sanjay G. Rao, Bruno Ribeiro 0001
SIGCOMM1
2023 Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback
abstract
When streaming 360° video, it is possible to reduce bandwidth by 5× with approaches that spatially segment video into tiles and only stream the user's viewport. Unfortunately, it is difficult to accurately predict a user's viewport even 2--3 seconds before playback. This results in rebuffering events owing to misprediction of a user's viewport or network bandwidth dips, which hurts interactive experience. However, avoiding rebuffering by naively skipping tiles that do not arrive by the playback deadline may lead to incomplete viewports and degraded experience.
Ehab Ghabashneh, Chandan Bothra, Ramesh Govindan, Antonio Ortega, Sanjay G. Rao
SIGCOMM2