@inproceedings{jiang-etal-2026-streamsense,
title = "{S}tream{S}ense: A Content- and Resource-Aware Edge Agent for Adaptive Bitrate Streaming",
author = "Jiang, Chuanqi and
Li, Linhan and
Tao, Zhiguo",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.60/",
pages = "823--838",
ISBN = "979-8-89176-478-1",
abstract = "Adaptive Bitrate (ABR) decision-making is essential for optimizing Quality of Experience (QoE) in video streaming. Traditional ABR methods typically lack fine-grained content understanding and face challenges in balancing multi-dimensional QoE objectives under real-world edge device constraints. To overcome these limitations, we propose **StreamSense Agent**, a novel framework that reformulates ABR as a sequential decision-making process informed by content and network awareness. Our approach introduces three key innovations: (1) a Dynamic Content-Aware Entity Graph (D-CAEG) for structured video representation, (2) a composite QoE-driven optimization objective integrating perceptual quality, rebuffering, smoothness, and computational cost, and (3) a hierarchical resource-aware architecture enabling real-time control through an efficient lightweight actor. We further adopt an offline-to-online co-adaptation training paradigm with federated learning for continuous adaptation. Since effective ABR control requires the same core competencies as general sequential decision-making{---}multi-step planning across future video chunks, dynamic information gathering from heterogeneous network signals, and adaptive strategy adjustment under resource constraints{---}we validate these transferable capabilities on five established benchmarks. Specifically, multi-hop search tasks (BrowseComp, BrowseComp-ZH) mirror the integration of dispersed network-trace and content-complexity signals into a bitrate plan; complex navigation tasks (WebWalkerQA) correspond to sequential buffer-state traversal under volatile bandwidth; and information-extraction tasks (Xbench-DS, SimpleQA) reflect the precision required when reading real-time streaming telemetry. StreamSense Agent achieves state-of-the-art results, attaining accuracy of 16.7{\%} on BrowseComp, 30.5{\%} on BrowseComp-ZH, 42.1{\%} on Xbench-DS, and 54.2{\%} on WebWalkerQA. Deployment-oriented evaluations further confirm its computational efficiency, achieving an inference throughput of 12.5 requests per second with a memory footprint of only 2,100 MB under edge-like constraints, yielding a normalized QoE score of 0.92. Comprehensive ablation studies validate the necessity of each proposed component. This work establishes a new paradigm for intelligent, edge-deployable bitrate adaptation."
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<abstract>Adaptive Bitrate (ABR) decision-making is essential for optimizing Quality of Experience (QoE) in video streaming. Traditional ABR methods typically lack fine-grained content understanding and face challenges in balancing multi-dimensional QoE objectives under real-world edge device constraints. To overcome these limitations, we propose **StreamSense Agent**, a novel framework that reformulates ABR as a sequential decision-making process informed by content and network awareness. Our approach introduces three key innovations: (1) a Dynamic Content-Aware Entity Graph (D-CAEG) for structured video representation, (2) a composite QoE-driven optimization objective integrating perceptual quality, rebuffering, smoothness, and computational cost, and (3) a hierarchical resource-aware architecture enabling real-time control through an efficient lightweight actor. We further adopt an offline-to-online co-adaptation training paradigm with federated learning for continuous adaptation. Since effective ABR control requires the same core competencies as general sequential decision-making—multi-step planning across future video chunks, dynamic information gathering from heterogeneous network signals, and adaptive strategy adjustment under resource constraints—we validate these transferable capabilities on five established benchmarks. Specifically, multi-hop search tasks (BrowseComp, BrowseComp-ZH) mirror the integration of dispersed network-trace and content-complexity signals into a bitrate plan; complex navigation tasks (WebWalkerQA) correspond to sequential buffer-state traversal under volatile bandwidth; and information-extraction tasks (Xbench-DS, SimpleQA) reflect the precision required when reading real-time streaming telemetry. StreamSense Agent achieves state-of-the-art results, attaining accuracy of 16.7% on BrowseComp, 30.5% on BrowseComp-ZH, 42.1% on Xbench-DS, and 54.2% on WebWalkerQA. Deployment-oriented evaluations further confirm its computational efficiency, achieving an inference throughput of 12.5 requests per second with a memory footprint of only 2,100 MB under edge-like constraints, yielding a normalized QoE score of 0.92. Comprehensive ablation studies validate the necessity of each proposed component. This work establishes a new paradigm for intelligent, edge-deployable bitrate adaptation.</abstract>
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%0 Conference Proceedings
%T StreamSense: A Content- and Resource-Aware Edge Agent for Adaptive Bitrate Streaming
%A Jiang, Chuanqi
%A Li, Linhan
%A Tao, Zhiguo
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F jiang-etal-2026-streamsense
%X Adaptive Bitrate (ABR) decision-making is essential for optimizing Quality of Experience (QoE) in video streaming. Traditional ABR methods typically lack fine-grained content understanding and face challenges in balancing multi-dimensional QoE objectives under real-world edge device constraints. To overcome these limitations, we propose **StreamSense Agent**, a novel framework that reformulates ABR as a sequential decision-making process informed by content and network awareness. Our approach introduces three key innovations: (1) a Dynamic Content-Aware Entity Graph (D-CAEG) for structured video representation, (2) a composite QoE-driven optimization objective integrating perceptual quality, rebuffering, smoothness, and computational cost, and (3) a hierarchical resource-aware architecture enabling real-time control through an efficient lightweight actor. We further adopt an offline-to-online co-adaptation training paradigm with federated learning for continuous adaptation. Since effective ABR control requires the same core competencies as general sequential decision-making—multi-step planning across future video chunks, dynamic information gathering from heterogeneous network signals, and adaptive strategy adjustment under resource constraints—we validate these transferable capabilities on five established benchmarks. Specifically, multi-hop search tasks (BrowseComp, BrowseComp-ZH) mirror the integration of dispersed network-trace and content-complexity signals into a bitrate plan; complex navigation tasks (WebWalkerQA) correspond to sequential buffer-state traversal under volatile bandwidth; and information-extraction tasks (Xbench-DS, SimpleQA) reflect the precision required when reading real-time streaming telemetry. StreamSense Agent achieves state-of-the-art results, attaining accuracy of 16.7% on BrowseComp, 30.5% on BrowseComp-ZH, 42.1% on Xbench-DS, and 54.2% on WebWalkerQA. Deployment-oriented evaluations further confirm its computational efficiency, achieving an inference throughput of 12.5 requests per second with a memory footprint of only 2,100 MB under edge-like constraints, yielding a normalized QoE score of 0.92. Comprehensive ablation studies validate the necessity of each proposed component. This work establishes a new paradigm for intelligent, edge-deployable bitrate adaptation.
%U https://aclanthology.org/2026.groundlm-1.60/
%P 823-838
Markdown (Informal)
[StreamSense: A Content- and Resource-Aware Edge Agent for Adaptive Bitrate Streaming](https://aclanthology.org/2026.groundlm-1.60/) (Jiang et al., GroundLM 2026)
ACL