The increasing volume of video traffic presents significant challenges to network transmission, while edge computing accelerates video delivery by leveraging caching and computation to optimize content forwarding. However, as edge computing is generally deployed by service providers in a transparent manner, clients cannot perceive edge states, e.g., cache availability, potentially resulting in suboptimal bitrate decisions. This issue persists even with intelligent bitrate selection approaches on the client side, as the inaccurate estimation of network delivery capacity due to edge cache transparency remains unresolved. Meanwhile, single-edge servers or nodes, with limited cache space and computational capacity for a small number of users, can be more effective by aggregating into clusters to better serve users and optimize resource utilization. Therefore, we propose an edge-assisted bitrate re-adaptation scheme (e-BitRead) for adaptive streaming, utilizing neighbor edges to accelerate video deliveries.e-BitReadintroduces three key innovations: (i) it employs a bitrate re-adaptation mechanism that intelligently selects alternative bitrates from edge servers instead of strictly responding with the requested bitrate, (ii) it utilizes collaborative caching across multiple edge servers to expand available bitrate options through coordinated resource sharing, and (iii) it enhances the learning efficiency through joint optimization of network architecture and reward design, which leverages actor-critic structure to fit into multi-edge bitrate adaptation. In experiments with an intelligent client ABR,e-BitReaddemonstrates its superiority by achieving a 1.43x higher hit ratio compared to the baseline, while improving QoE by 1.93x over the scheme without smart bitrate matching and delivering a 33% gain over the single-edge re-adaptation approach.

The rapid growth of video traffic imposes significant challenges on content delivery over the Internet. Meanwhile, edge computing is developed to accelerate video transmission as well as release the traffic load of origin servers. Although some related techniques (e.g., transcoding and prefetching) are proposed to improve edge services, they cannot fully utilize cached videos. Therefore, we propose a Learning-based Fuzzy Bitrate Matching scheme (LFBM) at the edge for adaptive video streaming, which utilizes the capacity of network and edge servers. In accordance with user requests, cache states and network conditions, LFBM utilizes reinforcement learning to make a decision, either fetching the video of the exact bitrate from the origin server or responding with a different representation from the edge server. In the simulation, compared with the baseline, LFBM improves cache hit ratio by 128%. Besides, compared with the scheme without fuzzy bitrate matching, it improves Quality of Experience (QoE) by 45%. Moreover, the real-network experiments further demonstrate the effectiveness of LFBM. It increases the hit ratio by 84% compared with the baseline and improves the QoE by 51% compared with the scheme without fuzzy bitrate matching.

Datacenters currently deploy shallow-buffered switches to achieve low latency by avoiding long waiting time in the data plane. However, the limited buffer space in the switch causes the frequent overflow and the notorious TCP incast problem. Moreover, the simple scheduling strategy in buffer deprives the switch of the ability to offer deeply differentiated services. Therefore, we present a novel priority-based flow-aware in-network caching scheme, named Poche, which supplies more control capabilities for the network side through introducing some additional cache resource into switches. Poche classifies network traffic into multiple priorities according to the latency requirements of flows. The end server adds priority tags to packets and sets different RTO values for flows with distinct priorities. The switch monitors the buffer utilization of each port and performs the priority-based flow-aware caching and injecting strategies based on the analysis of the scheduling model between the buffer and cache. We conduct comprehensive experiments to compare Poche with the state-of-the-art traffic optimization schemes. The results demonstrate that Poche can reduce the FCTs of latency-sensitive flows by at least 59.1% and improve the network throughput by at least 54.4%, while ensuring the finite cached volume and effectively addressing the incast problem.

The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.

Software-Defined Networking (SDN) provides a tractable and efficient architecture for operators to customize their network functions. Many traditional Data Center Networks (DCNs) are upgraded by SDN to improve link utilization and management flexibility, but they are lack of the instructions for selecting the substitutive SDN switches with the proper flow table space to achieve cost-effective and energy-saving networks. In this paper, we fill the gap of solving the flow table space estimation problem based on queuing theory. First, we divide the life process of a flow table entry into the packet-in process, the handling process and the serving process to establish a queuing system to estimate the least required number of the flow table entries of SDN switches. Second, we analyze the traffic distribution of DCNs to calculate the critical parameters in our model. Third, on the basis of the essence of the structured topologies in DCNs, we construct a probability model of routing strategies to quantize the influence of path selection. Comprehensive experiments show that the relative flow table space estimation error of our model can be less than 10%, which can give operators insights into the requirement of the SDN switches at specific positions.

th for various services. Yet today’s widely used load balancing scheme, i.e., ECMP, may cause serious congestion when hash collision happens. Recent proposals either push load balancing function to a centralized controller or network edges. However, the centralized schemes are too slow for latency-sensitive flows, while the distributed schemes lack the global view and usually cannot make the best choices. In this paper, based on Software-Defined Networking (SDN), we present a new hybrid load balancing scheme called BLEND. It promotes the cooperation among network components and takes advantage of both global view and fast end-host action. BLEND aims to improve the throughput of big flows and reduce the latency of small and medium flows. It employs a controller to assign paths to big flows to achieve high throughput. In addition, in order to provide guidance for fast distributed load balancing decisions, the controller also calculates the optimal network delay thresholds for small and medium flows, while hosts utilize these thresholds to decide whether to change the current paths. BLEND is practical and easily deployable in the current data center networks. Comprehensive experiments demonstrate that BLEND outperforms both the centralized and distributed schemes and achieves at most 40% reduction in FCT and at most 2.8 times improvement in throughput.

Remote Direct Memory Access (RDMA) can be deployed in Content Delivery Networks (CDN) Points of Presence (PoPs) to avoid the high CPU overheads caused by traditional TCP/IP stacks. However, RDMA cannot surmount the drawbacks of the window-based conservative of TCP and is insensitive to Quality of Experience (QoE). Moreover, the requirement of lossless networks hinders the widespread application of RDMA. In this paper, we introduce the parallel multipoint-to-multipoint Request-Grant-Transfer (RGT) mode into RDMA to solve the aforementioned problems. Compared with traditional RGT mode, our scheme supports parallel Dynamic Adaptive Streaming over HTTP (DASH) chunk delivery, thereby improving throughput and reducing initial delays. We differentiate the importance of DASH chunks according to QoE-related properties. In this way, we reduce the response time of specific DASH chunks. We provide an efficient approach to select the optimal number of requests for partially traversing pending requests to reduce the overheads of Request stages. We perform comprehensive experiments to demonstrate that our scheme improves the throughput of CDN PoPs and enhances client QoE.

Dynamic Adaptive Streaming over HTTP (DASH) has emerged as a popular approach for video transmission, which brings a potential benefit for the Quality of Experience (QoE) because of its segment-based flexibility. However, the Internet can only provide no guaranteed delivery. The high dynamic of the available bandwidth may cause bitrate switching or video rebuffering, thus inevitably damaging the QoE. Besides, the frequently requested popular videos are transmitted for multiple times and contribute to most of the bandwidth consumption, which causes massive transmission redundancy. Therefore, we propose a Learning-based Edge with cAching and Prefetching (LEAP) to improve the online user QoE of adaptive video streaming. LEAP introduces caching into the edge to reduce the redundant video transmission and employs prefetching to fight against network jitters. Taking the state information of users into account, LEAP intelligently makes the most beneficial decisions of caching and prefetching by a QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we deploy the implemented prototype of LEAP in both the simulated scenario and the real Internet. Compared with all selected schemes, LEAP at least raises average bitrate by 34.4% and reduces video rebuffering by 42.7%, which leads to at least 15.9% improvement in the user QoE in the simulated scenario. The results in the real Internet scenario further confirm the superiority of LEAP.

Deploying Software-Defined Networks (SDNs) faces various challenges, and one of them is to implement per-flow control while preserving data plane scalability. Due to the limited rule storage space of commodity SDN switches, achieving flexible control and having a low-latency data plane with a low storage cost are often at odds. Unfortunately, existing SDN architectures fail to implement per-flow control efficiently: they either incur extra delays to packets or pose high storage burden to switches. In this paper, we propose Software-Defined Label Switching (SDLS) to achieve both data plane scalability and per-flow control. SDLS combines central control with label switching to reduce storage burden while maintaining per-flow control. SDLS introduces software switches into the data plane and manages the network in regions for scalability. SDLS is OpenFlow-compatible and employs a hybrid data plane to provide efficient flow setups. We evaluate SDLS by comparing with the state-of-the-art SDN architectures and show that SDLS can rival the best on the latency performance while reducing the number of flow entries and overflows by more than 47%.