IEEE Transactions on Multimedia (TMM)

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Yuan Yuan (Southwest Jiaotong University, China), Hongjie He (Southwest Jiaotong University, China), Yaolin Yang (Southwest Jiaotong University, China), Hadi Amirpour (AAU, Klagenfurt, Austria), Christian Timmerer (AAU, Klagenfurt, Austria), Fan Chen (Southwest Jiaotong University, China)

Abstract: Existing JPEG encryption approaches pose a security risk due to the difficulty in changing all block-feature values while considering format compatibility and file size expansion. To address these concerns, this paper introduces a novel JPEG image encryption scheme. First, the security of sketch information against chosen-plaintext attacks is improved by increasing the change rate of block-feature values. Second, a classification global permutation approach is designed to encrypt the undivided run/size, value (RSV)-based AC groups to achieve larger changes in the block-feature values. Third, to reduce file size expansion while maintaining format compatibility, the DC coefficients are rotated based on the mapped DC differences in the same category, and the nonzero AC coefficients are mapped in the same category. Extensive experiments demonstrate that the proposed algorithm is superior to existing schemes in terms of security. Notably, the average change rate of block-feature values is increased by at least 20%. Furthermore, the proposed scheme reduces the file size by an average of 2.036% compared to existing JPEG image encryption methods.

IEEE Transactions on Network and Service Management 

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Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Ekrem Çetinkaya (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Shojafar (University of Surrey, UK), Mohammad Ghanbari (University of Essex, UK), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Recent years have witnessed video streaming demands evolve into one of the most popular Internet applications. With the ever-increasing personalized demands for high-definition and low-latency video streaming services, network-assisted video streaming schemes employing modern networking paradigms have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context. The emergence of such techniques addresses long-standing challenges of enhancing users’ Quality of Experience (QoE), end-to-end (E2E) latency, as well as network utilization. However, designing a cost-effective, scalable, and flexible network-assisted video streaming architecture that supports the aforementioned requirements for live streaming services is still an open challenge. This article leverage novel networking paradigms, i.e., edge computing and Network Function Virtualization (NFV), and promising video solutions, i.e., HAS, Video Super-Resolution (SR), and Distributed Video Transcoding (TR), to introduce A Latency- and cost-aware hybrId P2P-CDN framework for liVe video strEaming (ALIVE). We first introduce the ALIVE multi-layer architecture and design an action tree that considers all feasible resources (i.e., storage, computation, and bandwidth) provided by peers, edge, and CDN servers for serving peer requests with acceptable latency and quality. We then formulate the problem as a Mixed Integer Linear Programming (MILP) optimization model executed at the edge of the network. To alleviate the optimization model’s high time complexity, we propose a lightweight heuristic, namely, Greedy-Based Algorithm (GBA). Finally, we (i) design and instantiate a large-scale cloud-based testbed including 350 HAS players, (ii) deploy ALIVE on it, and (iii) conduct a series of experiments to evaluate the performance of ALIVE in various scenarios. Experimental results indicate that ALIVE (i) improves the users’ QoE by at least 22%, (ii) decreases incurred cost of the streaming service provider by at least 34%, (iii) shortens clients’ serving latency by at least 40%, (iv) enhances edge server energy consumption by at least 31%, and (v) reduces backhaul bandwidth usage by at least 24% compared to base line approaches.

Keywords: HTTP Adaptive Streaming (HAS); Edge Computing; Network Function Virtualization (NFV); Content Delivery Network (CDN); Peer-to-Peer (P2P); Quality of Experience (QoE); Video Transcoding; Video Super-Resolution.

Special Issue on Sustainable Multimedia Communications and Services, IEEE COMSOC MMTC Communications – Frontiers,

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Reza Farahani(Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Streaming segmented videos over the Hypertext Transfer Protocol (HTTP) is an increasingly popular approach in both live and video-on-demand (VoD) applications. However, designing a scalable and adaptable framework that reduces servers’ energy consumption and supports low latency and high quality services, particularly for live video streaming scenarios, is still challenging for Over-The-Top (OTT) service providers. To address such challenges, this paper introduces a new hybrid P2P-CDN framework that leverages new networking and computing paradigms, i.e., Network Function Virtualization (NFV) and edge computing for live video streaming. The proposed framework introduces a multi-layer architecture and a tree of possible actions therein (an action tree), taking into account all available resources from peers, edge, and CDN servers to efficiently distribute video fetching and transcoding tasks across a hybrid P2P-CDN network, consequently enhancing the users’ latency and video quality. We also discuss our testbed designed to validate the framework and compare it with baseline methods. The experimental results indicate that the proposed framework improves user Quality of Experience (QoE), reduces client serving latency, and improves edge server energy consumption compared to baseline approaches.

Keywords: Energy Efficiency; HAS; DASH; Edge Computing; NFV; CDN; P2P; Low Latency; QoE; Video Transcoding.

We’re excited to share that the ACM Special Interest Group in Multimedia (SIGMM) presents to

Stefan Lederer, Christopher Müller, and Christian Timmerer

The SIGMM Test of Time Paper Honorable Mention in the category of “MM Systems & Networking”

for their paper “Dynamic Adaptive Streaming over HTTP Dataset”. In Proceedings of the 3rd Multimedia Systems Conference, MMSys ’12, page 89–94, New York, NY, USA, 2012. ACM. doi:10.1145/2155555.2155570

The 19th International Conference on emerging Networking EXperiments and Technologies

Leonardo Peroni (IMDEA Networks Institute and UC3M), Sergey Gorinsky (IMDEA Networks Institute), Farzad Tashtarian (AAU, Austria), and Christian Timmerer (AAU, Austria).

Abstract: Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.