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1. 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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2. QoE Inference and Improvement Without End-Host Control

   https://doi.org/10.1109/SEC.2018.00011  

   Nikravesh, Ashkan; Chen, Qi Alfred; Haseley, Scott; Zhu, Xiao; Challen, Geoffrey; Mao, Z. Morley (October 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC))

   Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address this problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app's network traffic-including ISPs and access points-to infer the app's QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%. 

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3. GreenABR+: Generalized Energy-Aware Adaptive Bitrate Streaming

   https://doi.org/10.1145/3649898  

   Turkkan, Bekir Oguzhan; Dai, Ting; Raman, Adithya; Kosar, Tevfik; Chen, Changyou; Bulut, Muhammed; Zola, Jaroslav; Sow, Daby (September 2024, ACM Transactions on Multimedia Computing, Communications, and Applications)

   Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This article proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization. 

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4. A Hybrid NDN-IP Architecture for Live Video Streaming: From Host-Based to Content-Based Delivery to Improve QoE

   https://doi.org/10.1142/S1793351X22400074  

   Dasgupta, Ishita; Shannigrahi, Susmit; Zink, Michael (June 2022, International Journal of Semantic Computing)

   With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying Internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner. Based on a prototype, we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, as opposed to IP-only, our hybrid setup achieves better average bitrate and better perceived visual quality (computed via VMAF metric) over live video streaming services. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE. 

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5. A hybrid NDN-IP Architecture for Live Video Streaming: A QoE Analysis

   https://doi.org/10.1109/ISM52913.2021.00032  

   Dasgupta, Ishita; Shannigrahi, Susmit; Zink, Michael (November 2021, IEEE International Symposium on Multimedia (ISM))

   With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner. Based on a prototype we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, our hybrid setup achieves better average bitrate over live video streaming services than its IP-only alternative. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE. 

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