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1. Toward QoE-based Routing Path Selection

   https://doi.org/10.1109/HPSR57248.2023.10147938  

   Kulkarni, Umakant ; Chen, Yufeng ; Melampy, Patrick ; Fahmy, Sonia ( June 2023 , 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR))

   The increasing popularity of video streaming and conferencing services have altered the nature of Internet traffic. In this paper, we take a first step toward quantifying the impact of this changing nature of traffic on the Quality of Experience (QoE) of popular video streaming and conferencing applications. We first analyze the traffic characteristics of these applications and of backbone links, and show how simple multipath routing may adversely impact application QoE. To mitigate this problem, we propose a new routing path selection approach, inspired by the TCP timeout computation algorithm, that uses both the average and variation of path load. Preliminary results show that this approach improves application QoE by on average 14% and packet latency by 11% for video streaming and conferencing applications, respectively. 

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2. 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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3. Fresher content or smoother playback?: a brownian-approximation framework for scheduling real-time wireless video streams

   https://doi.org/10.1145/3397166.3409121  

   Hsieh, Ping-Chun ; Liu, Xi ; Hou, I-Hong ( October 2020 , ACM MobiHoc 2020)

   null (Ed.)

   This paper presents a Brownian-approximation framework to optimize the quality of experience (QoE) for real-time video streaming in wireless networks. In real-time video streaming, one major challenge is to tackle the natural tension between the two most critical QoE metrics: playback latency and video interruption. To study this trade-off, we first propose an analytical model that precisely captures all aspects of the playback process of a real-time video stream, including playback latency, video interruptions, and packet dropping. Built on this model, we show that the playback process of a real-time video can be approximated by a two-sided reflected Brownian motion. Through such Brownian approximation, we are able to study the fundamental limits of the two QoE metrics and characterize a necessary and sufficient condition for a set of QoE performance requirements to be feasible. We propose a scheduling policy that satisfies any feasible set of QoE performance requirements and then obtain simple rules on the trade-off between playback latency and the video interrupt rates, in both heavy-traffic and under-loaded regimes. Finally, simulation results verify the accuracy of the proposed approximation and show that the proposed policy outperforms other popular baseline policies. 

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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. 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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