Patrick Seeling

Performance evaluation and comparison of RObust Header Compression (ROHC) ROHCv1 and ROHCv2 for multimedia delivery

In this paper the two implementations of RObust Header Compression (ROHC), namely ROHCv1 (RFC 3095) and ROHCv2 (RFC 5225), are evaluated in terms of potential throughput increase and complexity for wireless IP networks and compared with each other for different multimedia streams. In order to compress the header information to a minimum, RoHCv1 has been proposed some years back and recently RoHCv2 has been proposed with a complete redesign compared to its predecessor. The novelty of this paper is the presentation of the performance evaluation of ROHCv2 as well as a comparison between the two approaches that have never been published before. In addition, this paper presents real-world performance measurements of ROHCv2 for the first time. Our findings show that both versions have great potential in saving bandwidth for multimedia traffic in wireless networks, even with varying channel conditions by an IP/UDP/RTP header a compression gain of 83% and 86% is possible for ROHCv1 and ROHCv2, respectively. While the second version of ROHC is giving generally slightly better compression ratios than the first version, we found situations where the first version performs better.

Performance Evaluation and Implementation of IP and Robust Header Compression Schemes for TCP and UDP Traffic in the Wireless Context

Modern cellular networks utilizing the long-term evolution (LTE) set of standards face an ever-increasing demand for mobile data from connected devices and header compression is employed to minimize the overhead for IP-based cellular network traffic. In this paper, we evaluate the three implementations of header compressions used by these networks with respect to their potential throughput increase and complexity for different mobile service scenarios over wireless IP networks. Specifically, we consider header compression as defined by (i) IP Header Compression (RFC 2507), (ii) Robust Header Compression version 1 (RFC 3095), and (iii) the recently updated Robust Header Compression version 2 (RFC 5225) with TCP/IP profile (RFC 6846). The novelty of this paper is the presentation of the performance evaluation of IP Header Compression for UDP and TCP, as well as a comparison between the two compression methods. Our findings show that all implementations have great potential for saving bandwidth in IP-based wireless networks, even under varying channel conditions. While the RoHC versions generally provide more reliable results than IPHC, we find that on a unidirectional channel, IPHC could perform better.

Efficiency Gain for RoHC Compressor Implementations with Dynamic Configuration

Modern cellular networks utilising the long-term evolution (LTE) and the coming 5G set of standards face an ever-increasing demand for low-latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. Since Robust Header Compression, among others, is primarily designed for the compression of live audio transmissions on endpoint devices, it performs best if certain fields, like the IP ID and RTP Timestamp, stay constant or change at a predetermined rate. Moreover, the compressor expects the uncompressed stream to be loss-free as well, which might not be the case in general scenarios. We employ machine learning approaches for the prediction of Robust Header Compression version 1s and version 2s compression utility under various loss rates and header field dynamics. We analyse how the compressions react to different fluctuations in the headers and choose the compressor configuration which maximises utility. We show that the appropriate choice of compressor repetition configuration increases the overall utility under dynamic channel conditions, such as in the case of remote steering and various IoT applications, and finding the optimal configuration could produce significant benefits, like 1.2 speed-up in a fully utilised network.

Applying Robust Header Compression Version 2 for UDP and RTP Broadcasting with Field Constraints

Next generation applications of wireless IP networks face an ever increasing demand of real-time dissemination of sensory and similar data to nearby devices. During situations when the transmission capabilities become scarce due to overused bandwidth, such as vehicular and various IoT use-cases, limiting the number of transmitted bytes over the wireless interfaces could ease the network load considerably. One of the main potential approaches for reducing the traffic in such scenarios is broadcasting, which could be even more efficient when paired with compression approaches, such as header compression. Normally header compression is employed to minimise the overhead of IP-based cellular traffic between two directly connected peers. However, in broadcasting scenarios, the compression has to balance the trade- off between servicing the least reliable channels (i.e., robustness to losses) and performing at peak compression rates (generating smaller packet headers). In order to circumvent these limitations, we propose a constraint on various IP and RTP fields when employing the industry-standard Robust Header Compression version 2 (which was designed for direct links) in a broadcast scenario without modifications of the underlying mechanisms, i.e., with full RFC compliance. With this approach the compression becomes impervious to packet losses up to 50% and for UDP profile compression with IPv6 it is practically unaffected by the link quality. In turn the compression retains at least 50% efficiency but can be as high as 90%.

Robust Header Compression version 2 power consumption on Android devices via tunnelling

Next generation use-cases of wireless IP networks, including especially the Massive Machine Type Communications and IoT applications for the distribution of sensory and similar data will require the capability to handle large number of connections while maintaining a low-power footprint in order to function efficiently during long-term deployments. The reduction of the packetisation overhead resulting from the adaptation of header compression could potentially decrease the battery usage of network heavy applications via diminished wireless interface activity. Normally header compression is employed to minimise the overhead of IP-based cellular traffic between two connected peers. This paper presents for the first time comparative power consumption measurements for Robust Header Compression version 2 (RoHCv2) using WiFi and LTE. We find that the adoption of header compression on modern mobile devices will generally not result in increased power consumption based on the extra complexity added by the execution of the algorithms. We also show that the usage of RoHCv2 can potentially even decrease the battery drain on average by about 0.05 W when payloads are small.

Regression Model Building and Efficiency Prediction of RoHCv2 Compressor Implementations for VoIP

Modern cellular networks utilising the long-term evolution (LTE) and the coming 5G set of standards face an ever-increasing demand for low-latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. We employ machine learning approaches for the prediction of Robust Header Compression version 2s (RFC 5225) compression utility for VoIP transmissions, which enables the compression to dynamically adapt to varying channel conditions. We evaluate the prediction models employing R^2 and mean square error scores next to complexity (number of coefficients) based on an RTP specific training data set and a separately captured live VoIP audio call. We find that the proposed weighted Ridge regression model explains about 70% of the training data and 72% of a separate VoIP transmissions utility. This approach outperforms the Ridge and first-order Bayesian regressions by up to 50% and the second and third order regressions utilising polynomial basis functions by up to 20%, making it well-suited for utility estimation.