Towards SVC-based adaptive streaming in information centric networks

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE

Complete mesocolic excision does not increase short-term complications in laparoscopic left-sided colectomies : a comparative retrospective single-center study

Background: Since the implementation of total mesorectal excision (TME) in rectal cancer surgery, oncological outcomes improved dramatically. With the technique of complete mesocolic excision (CME) with central vascular ligation (CVL), the same surgical principles were introduced to the field of colon cancer surgery. Until now, current literature fails to invariably demonstrate its oncological superiority when compared to conventional surgery, and there are some concerns on increased morbidity. The aim of this study is to compare short-term outcomes after left-sided laparoscopic CME versus conventional surgery. Methods: In this retrospective analysis, data on all laparoscopic sigmoidal resections performed during a 3-year period (October 2015 to October 2018) at our institution were collected. A comparative analysis between the CME group-for sigmoid colon cancer-and the non-CME group-for benign disease-was performed. Results: One hundred sixty-three patients met the inclusion criteria and were included for analysis. Data on 66 CME resections were compared with 97 controls. Median age and operative risk were higher in the CME group. One leak was observed in the CME group (1/66) and 3 in the non-CME group (3/97), representing no significant difference. Regarding hospital stay, postoperative complications, surgical site infections, and intra-abdominal collections, no differences were observed. There was a slightly lower reoperation (1.5% versus 6.2%, p = 0.243) and readmission rate (4.5% versus 6.2%, p = 0.740) in the CME group during the first 30 postoperative days. Operation times were significantly longer in the CME group (210 versus 184 min, p < 0.001), and a trend towards longer pathological specimens in the CME group was noted (21 vs 19 cm, p = 0.059). Conclusions: CME does not increase short-term complications in laparoscopic left-sided colectomies. Significantly longer operation times were observed in the CME group

Proactive multi-tenant cache management for virtualized ISP networks

The content delivery market has mainly been dominated by large Content Delivery Networks (CDNs) such as Akamai and Limelight. However, CDN traffic exerts a lot of pressure on Internet Service Provider (ISP) networks. Recently, ISPs have begun deploying so-called Telco CDNs, which have many advantages, such as reduced ISP network bandwidth utilization and improved Quality of Service (QoS) by bringing content closer to the end-user. Virtualization of storage and networking resources can enable the ISP to simultaneously lease its Telco CDN infrastructure to multiple third parties, opening up new business models and revenue streams. In this paper, we propose a proactive cache management system for ISP-operated multi-tenant Telco CDNs. The associated algorithm optimizes content placement and server selection across tenants and users, based on predicted content popularity and the geographical distribution of requests. Based on a Video-on-Demand (VoD) request trace of a leading European telecom operator, the presented algorithm is shown to reduce bandwidth usage by 17% compared to the traditional Least Recently Used (LRU) caching strategy, both inside the network and on the ingress links, while at the same time offering enhanced load balancing capabilities. Increasing the prediction accuracy is shown to have the potential to further improve bandwidth efficiency by up to 79%

Cooperative announcement-based caching for video-on-demand streaming

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

Dynamic scheduling of computationally intensive applications on unreliable infrastructures

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Design and evaluation of a self-learning HTTP adaptive video streaming client

HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment