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1. GreenABR: Energy-aware Adaptive Bitrate Streaming with Deep Reinforcement Learning

   Turkkan, B.; Dai, T.; Raman, A.; Kosar, T.; Chen, C.; Bulut, F.; Zola, Z.; Sow, D. (June 2022, Proceedings of the 13th ACM Multimedia Systems Conference (MMSys) 2022)

   Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations. 

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2. GreenABR: energy-aware adaptive bitrate streaming with deep reinforcement learning

   https://doi.org/10.1145/3524273.3528188  

   Turkkan, Bekir Oguzhan; Dai, Ting; Raman, Adithya; Kosar, Tevfik; Chen, Changyou; Bulut, Muhammed Fatih; Zola, Jaroslaw; Sow, Daby (June 2022, Proceedings of the 13th ACM Multimedia Systems Conference)

   Adaptive bitrate (ABR) algorithms aim to make optimal bitrate de- cisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video stream- ing. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming pa- rameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR’s deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations. 

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3. Grad: Learning for Overhead-aware Adaptive Video Streaming with Scalable Video Coding

   https://doi.org/10.1145/3394171.3413512  

   Liu, Yunzhuo; Jiang, Bo; Guo, Tian; Sitaraman, Ramesh K.; Towsley, Don; Wang, Xinbing (October 2020, ACM Multimedia Conference (MM'20))

   null (Ed.)

   Video streaming commonly uses Dynamic Adaptive Streaming over HTTP (DASH) to deliver good Quality of Experience (QoE) to users. Videos used in DASH are predominantly encoded by single-layered video coding such as H.264/AVC. In comparison, multi-layered video coding such as H.264/SVC provides more flexibility for up- grading the quality of buffered video segments and has the potential to further improve QoE. However, there are two challenges for us- ing SVC in DASH: (i) the complexity in designing ABR algorithms; and (ii) the negative impact of SVC’s coding overhead. In this work, we propose a deep reinforcement learning method called Grad for designing ABR algorithms that take advantage of the quality up- grade mechanism of SVC. Additionally, we quantify the impact of coding overhead on the achievable QoE of SVC in DASH, and propose jump-enabled hybrid coding (HYBJ) to mitigate the impact. Through emulation, we demonstrate that Grad-HYBJ, an ABR algo- rithm for HYBJ learned by Grad, outperforms the best performing state-of-the-art ABR algorithm by 17% in QoE. 

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4. Optimal Strategies for Live Video Streaming in the Low-latency Regime

   https://doi.org/10.1109/ICNP.2019.8888127  

   Sun, Liyang; Zong, Tongyu; Liu, Yong; Wang, Yao; Zhu, Haihong (October 2019, IEEE 27th International Conference on Network Protocols (ICNP))

   Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live video streaming by developing dynamic models and optimal control strategies. We further develop practical live video streaming algorithms within the Model Predictive Control (MPC) framework, namely MPC-Live, to maximize user QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live video streaming algorithms can improve the performance dramatically within latency range of two to five seconds. 

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5. NeRFlow: Towards Adaptive Streaming for NeRF Videos

   Zhang, Rui-Xiao; Huang, Tianchi; Chen, Bo; Nahrstedt, Klara (June 2025, ACM)

   Neural Radiance Field (NeRF) has emerged as a powerful technique for 3D scene representation due to its high rendering quality. Among its applications, mobile NeRF video-on-demand (VoD) is especially promising, beneting from both the scalability of the mobile devices and the immersive experience oered by NeRF. However, streaming NeRF videos over real-world networks presents signi cant challenges, particularly due to limited bandwidth and temporal dynamics. To address these challenges, we propose NeRFlow, a novel framework that enables adaptive streaming for NeRF videos through both bitrate and viewpoint adaptation. NeRFlow solves three fundamental problems: rst, it employs a rendering-adaptive pruning technique to determine voxel importance, selectively reducing data size without sacricing rendering quality. Second, it introduces a viewpoint-aware adaptation module that eciently compensates for uncovered regions in real time by combining preencoded master and sub-frames. Third, it incorporates a QoE-aware bitrate ladder generation framework, leveraging a genetic algorithm to optimize the number and conguration of bitrates while accounting for bandwidth dynamics and ABR algorithms. Through extensive experiments, NeRFlow is demonstrated to eectively improve user Quality of Experience (QoE) by 31.3% to 41.2%, making it an ecient solution for NeRF video streaming. 

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