Browsing by Author "Gazi Karam Illahi, Gazi"

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Cloud Gaming With Foveated Video Encoding
(2020-03) Gazi Karam Illahi, Gazi; van Gemert, Thomas; Siekkinen, Matti; Masala, Enrico; Oulasvirta, Antti; Ylä-Jääski, Antti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Cloud gaming enables playing high-end games, originally designed for PC or game console setups, on low-end devices such as netbooks and smartphones, by offloading graphics rendering to GPU-powered cloud servers. However, transmitting the high-resolution video requires a large amount of network bandwidth, even though it is a compressed video stream. Foveated video encoding (FVE) reduces the bandwidth requirement by taking advantage of the non-uniform acuity of human visual system and by knowing where the user is looking. Based on a consumer-grade real-time eye tracker and an open source cloud gaming platform, we provide a cloud gaming FVE prototype that is game-agnostic and requires no modifications to the underlying game engine. In this article, we describe the prototype and its evaluation through measurements with representative games from different genres to understand the effect of parametrization of the FVE scheme on bandwidth requirements and to understand its feasibility from the latency perspective. We also present results from a user study on first-person shooter games. The results suggest that it is possible to find a "sweet spot" for the encoding parameters so the users hardly notice the presence of foveated encoding but at the same time the scheme yields most of the achievable bandwidth savings.
D2D-Enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming
(2019-08-12) Mehrabidavoodabadi, Abbas; Siekkinen, Matti; Gazi Karam Illahi, Gazi; Ylä-Jääski, Antti
A4 Artikkeli konferenssijulkaisussa
Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network. Direct device-to-device (D2D) communication can further alleviate load from the backhaul network. Both MEC and D2D have already been examined by prior work, but their combination applied to adaptive video streaming have not yet been explored in detail. In this paper, we analyze how enabling D2D jointly with edge computing affects the quality of experience (QoE) of video streaming clients and contributes to reducing the backhaul traffic. To this end, we formulate the problem of jointly maximizing the QoE of the clients and minimizing the backhaul traffic and edge processing as an integer non-linear programming (INLP) optimization model and propose a low-complexity algorithm using self-parameterization technique to solve the problem. The main takeaway from simulation results is that enabling D2D with edge computing reduces the backhaul traffic by approximately 18% and edge processing by 30% on average while maintaining roughly the same average video bitrate per client compared to edge computing without D2D. Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.
On the Interplay of Foveated Rendering and Video Encoding
(2020-11-01) Gazi Karam Illahi, Gazi; Siekkinen, Matti; Kämäräinen, Teemu; Ylä-Jääski, Antti
A4 Artikkeli konferenssijulkaisussa
Humans have sharp central vision but low peripheral visual acuity. Prior work has taken advantage of this phenomenon in two ways: foveated rendering (FR) reduces the computational workload of rendering by producing lower visual quality for peripheral regions and foveated video encoding (FVE) reduces the bitrate of streamed video through heavier compression of peripheral regions. Remote rendering systems require both rendering and video encoding and the two techniques can be combined to reduce both computing and bandwidth consumption. We report early results from such a combination with remote VR rendering. The results highlight that FR causes large bitrate overhead when combined with normal video encoding but combining it with FVE can mitigate it.
Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions
(2021-01-01) Jedari Ghourichaei, Behrouz; Premsankar, Gopika; Gazi Karam Illahi, Gazi; Di Francesco, Mario; Mehrabi, Abbas; Ylä-Jääski, Antti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.