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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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Application-based QoE support with P4 and OpenFlow
Although Software-Defined Wide Area Networks (SD-WANs) are now widely deployed in several production networks, they are largely restricted to traffic engineering ap- proaches based on layer 4 (L4) of the network protocol stack. Such approaches result in improved Quality-of-Service (QoS) of the network overall without necessarily focussing on the requirements of a specific application. However, the emergence of application protocols such as QUIC and HTTP/2 needs an in- vestigation of layer 5-based (L5) approaches in order to improve users’ Quality-of-Experience (QoE). In this paper, we leverage the capabilities of flexible, P4-based switches that incorporate protocol-independent packet processing in order to intelligently route traffic based on application headers. We use Adaptive Bit Rate (ABR) video streaming as an example to show how such an approach can not only provide flexible traffic management but also improve application QoE. Our evaluation consists of an actual deployment in a research testbed, Chameleon, where we leverage the benefits of fast paths in order to retransmit video segments in higher qualities. Further, we analyze real-world ABR streaming sessions from a large-scale CDN and show that our approach can successfully maximize QoE for all users in the dataset.
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- Award ID(s):
- 1743358
- PAR ID:
- 10107210
- Date Published:
- Journal Name:
- IEEE Conference on Computer Communications workshops
- ISSN:
- 2159-4228
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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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.more » « less
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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.more » « less
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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.more » « less
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