The Internet of Things (IoT) is considered a milestone for the realization of intelligent services through the interaction of smart things. Constrained Application Protocol (CoAP) was proposed by the Internet Engineering Task Force (IETF) as a web transfer protocol in the IoT. CoAP inherits the properties of the Representational State Transfer (REST) model to realize communications with low overhead. It is characterized by a small overhead and it uses the stateless UDP protocol in the transport layer. As the IoT has attracted more attention, the demands for various multimedia applications based on the IoT have increased. Video applications and IoT objects communicate through CoAP. CoAP defines a simple congestion control mechanism that can provide reliable communication between IoT endpoints. However, the congestion control mechanism was not proposed considering video streaming applications. In this paper, we evaluate the performance of video streaming applications for reliable CoAP communications between devices over a wireless network. To evaluate the performance, we perform multiple experiments by adjusting the CoAP transmission parameters. Moreover, we analyzed the effect of segment duration of a video being streamed over CoAP. The results show that the default CoAP parameters do not fulfill the stringent Quality of Experience (QoE) constraints. The experiments show that the default retransmission RTO is extremely high for the video streaming application. A small increase in the packet loss can significantly affect the video download time which increases the risk of playback interruption. Through experiments, we show that the performance of video streaming applications can be improved by tuning the congestion control parameters according to the network conditions. A smaller RTO value can ensure significant reduction in the segment download time. Similarly, the video can be downloaded quickly by increasing the number of unacknowledged transactions. Furthermore, the experiments show that the risk of playback interruption can be mitigated by downloading longer segments.

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%–12.3%, 8%–65%, 3.3%–5.7%, and 60%–130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

In networks, streaming of multimedia content is a challenging issue. Video streaming services make up a large proportion of Internet traffic throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Recently, to guarantee a promised quality of experience (QoE), multiple rate adaptive algorithms have been proposed. In this paper, a rate adaptive algorithm is proposed that improves video quality by observing the available throughput and managing the playback buffer. The existing schemes download the segments quickly to avoid playback interruption which may fill the buffer with low quality segments and degrade the user experience. The proposed scheme manages the playback buffer and keeps the buffer filled with high quality segments. Furthermore, the proposed algorithm randomizes the segment request time to mitigate the client’s biased view of network conditions in the presence of competing TCP traffic. The experimental results show that the proposed algorithm significantly improves the video streaming quality.